Job Description
BI Analyst / Senior Consultant – Business Intelligence & AI
Job Title
BI Analyst / Senior Consultant – BI & AI
Experience
6–9 Years
Location
Hybrid / Remote
Job Summary
We are seeking an experienced BI Analyst / Senior Consultant with 6–9 years of hands-on expertise in business intelligence, advanced analytics, and data engineering — combined with growing exposure to AI/LLM application development. The ideal candidate brings strong proficiency in SQL, Python, and PySpark for data processing, alongside deep BI platform expertise in Power BI or Tableau. They will have experience building governed semantic layers, developing scalable data pipelines, and integrating Large Language Model (LLM) capabilities into analytics workflows. This role sits at the intersection of traditional BI and next-generation AI-augmented analytics, making it ideal for a technically strong consultant ready to lead complex data initiatives.
Key Responsibilities
Data Engineering & Pipeline Development
- Write complex SQL queries, stored procedures, and optimized transformations across platforms such as Snowflake, Azure Synapse, BigQuery, or Redshift.
- Develop and maintain scalable data pipelines using Python and PySpark for large-scale batch and near-real-time data processing.
- Build and manage ELT/ETL workflows using dbt, ADF, Airflow, or Fivetran to ingest structured and semi-structured data.
- Implement Spark-based data processing on Databricks or Azure HDInsight for high-volume analytics workloads.
- Optimize query performance through partitioning, clustering, caching strategies, and execution plan analysis.
BI Development & Reporting
- Design, develop, and deploy enterprise-grade dashboards and reports using Power BI (DAX, Power Query, Composite Models) and Tableau.
- Build semantic models, calculated measures, KPI frameworks, and row-level security (RLS) configurations in Power BI or Looker.
- Develop LookML models, explores, and views in Looker to expose governed data layers for self-service analytics.
- Optimize BI report performance through DirectQuery tuning, aggregation tables, and incremental refresh strategies.
- Lead and mentor junior analysts in BI development standards, DAX best practices, and data modeling techniques.
Semantic Layer & Dimensional Modeling
- Design and maintain enterprise semantic models using dbt Semantic Layer, Power BI Semantic Models, Cube.dev, or AtScale.
- Build dimensional models (Star Schema, Snowflake Schema) with fact and dimension tables optimized for analytical query patterns.
- Define and standardize reusable business metrics, KPIs, hierarchies, and dimensions across reporting platforms.
- Ensure metric consistency and single source of truth across BI, dashboards, and AI-driven outputs.
AI & LLM Application Development (Exposure Required)
- Develop or contribute to AI-powered analytics applications using LLM APIs such as OpenAI GPT-4, Azure OpenAI, or Anthropic Claude.
- Build Retrieval-Augmented Generation (RAG) pipelines using frameworks such as LangChain or LlamaIndex to enable natural language querying over structured and unstructured data.
- Integrate LLM-generated insights, AI summaries, and conversational BI interfaces into existing Power BI or Tableau reporting workflows.
- Use Python libraries (openai, langchain, transformers, sentence-transformers) to prototype and deploy AI-driven analytics features.
- Implement vector search and embedding-based retrieval using tools such as FAISS, Pinecone, or Azure AI Search to surface contextual data insights.
- Contribute to prompt engineering, fine-tuning strategies, and evaluation frameworks for LLM outputs in analytics contexts.
- Explore and apply AI-native BI capabilities such as Power BI Copilot, Tableau Pulse, and Looker Explore AI.
Advanced Analytics & Data Science Integration
- Perform exploratory data analysis (EDA) using Python (pandas, numpy, matplotlib, seaborn, plotly) to surface trends and business insights.
- Collaborate with data science teams to integrate ML model outputs (e.g., churn scores, forecasts, classification results) into BI reporting layers.
- Develop statistical analyses, cohort analyses, and A/B test result reporting to support business experimentation.
- Apply time-series analysis and forecasting techniques using Python (statsmodels, Prophet, scikit-learn) for business planning use cases.
Stakeholder Engagement & Consulting
- Act as a senior analytical advisor to business stakeholders, translating complex data findings into clear business narratives.
- Lead requirement-gathering workshops, solution design sessions, and stakeholder demos for BI and AI analytics initiatives.
- Document functional and technical specifications for data pipelines, semantic models, and BI solutions.
- Participate in agile delivery — sprint planning, stand-ups, retrospectives — and manage delivery timelines for analytics workstreams.
Data Quality & Governance
- Implement data quality frameworks using dbt tests, Great Expectations, or custom SQL-based validation rules.
- Maintain data lineage, documentation, and metadata cataloging using tools such as Microsoft Purview, Alation, or dbt Docs.
- Define and enforce data governance standards, access control policies, and compliance requirements across BI and data assets.
Required Skills
Programming & Query Languages
- SQL — Advanced: CTEs, window functions, query optimization, stored procedures, dynamic SQL
- Python — Proficient: pandas, numpy, matplotlib, seaborn, sqlalchemy, requests, pyspark
- PySpark — Experience with distributed data processing, DataFrame API, Spark SQL, and UDFs
- DAX — Advanced: calculated columns, measures, time intelligence, row-level security
- LookML — Experience building models, explores, and views in Looker
- Shell scripting / Bash for pipeline automation and environment management
BI & Visualization Platforms
- Power BI — Advanced: Desktop, Service, Dataflows, Composite Models, Deployment Pipelines
- Tableau — Proficient: calculated fields, LOD expressions, Tableau Prep, Tableau Server
- Looker / LookML
- Sigma Computing or ThoughtSpot (preferred)
Data Platforms & Cloud
- Snowflake — Warehouses, clustering, materialized views, Snowpipe, dynamic data masking
- Azure: Azure Synapse Analytics, Azure Data Factory, Azure Databricks, Azure SQL
- AWS: Redshift, Glue, S3, Athena (preferred)
- GCP: BigQuery, Dataflow, Looker (preferred)
- Databricks — Delta Lake, Unity Catalog, MLflow (preferred)
Data Engineering & Integration Tools
- dbt (Core / Cloud) — models, tests, macros, seeds, snapshots, semantic layer
- Apache Airflow — DAG development, scheduling, operators
- Fivetran / Matillion / Azure Data Factory for data ingestion
- Apache Kafka or Azure Event Hubs for streaming data (preferred)
AI & LLM Technologies (Exposure Required)
- LLM APIs: OpenAI GPT-4 / GPT-4o, Azure OpenAI Service, Anthropic Claude
- LangChain or LlamaIndex for RAG pipeline development
- Vector databases: FAISS, Pinecone, Weaviate, or Azure AI Search
- Python AI libraries: openai, transformers, sentence-transformers, tiktoken
- Prompt engineering, context management, and chain-of-thought techniques
- Familiarity with AI-native BI tools: Power BI Copilot, Tableau Pulse, Looker Explore AI
- Microsoft Fabric or Azure AI Foundry exposure (preferred)
Semantic Layer Technologies
- dbt Semantic Layer / MetricFlow
- Power BI Semantic Models (Tabular / XMLA endpoint)
- Cube.dev or AtScale
- Snowflake Semantic Model (preferred)
DevOps & Delivery
- Git / GitHub / Azure DevOps — branching, pull requests, CI/CD pipelines
- Docker basics for containerized analytics environments
- Agile / Scrum delivery methodology
- JIRA / Azure Boards for sprint and backlog management
Preferred Qualifications
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
- Microsoft Certified: Power BI Data Analyst Associate (PL-300).
- Snowflake SnowPro Core or Advanced: Data Engineer Certification.
- Databricks Certified Associate Developer for Apache Spark.
- dbt Certified Developer (preferred).
- Experience in a consulting, professional services, or client-facing delivery environment.
- Hands-on experience building end-to-end AI/LLM-powered analytics applications.
Nice to Have
- Experience with Microsoft Fabric (OneLake, Fabric Notebooks, Real-Time Analytics).
- Exposure to MLOps practices: model versioning, monitoring, and deployment pipelines using MLflow or Azure ML.
- Knowledge of Data Vault 2.0 modeling methodology.
- Experience with real-time streaming analytics using Kafka, Spark Streaming, or Azure Stream Analytics.
- Familiarity with graph databases or knowledge graphs for AI-enhanced search.
- Exposure to data observability tools such as Monte Carlo, Anomalo, or dbt Artifacts.
Key Competencies
- Technical depth with the ability to move fluidly between SQL, Python, PySpark, and BI tooling.
- Strong analytical thinking and data-driven problem solving.
- Excellent communication skills — ability to present complex technical findings to non-technical stakeholders.
- Business acumen and senior stakeholder management in consulting environments.
- Curiosity and adaptability toward AI/LLM technologies and emerging analytics platforms.
- Collaborative mindset across data engineering, data science, and business teams.
- Attention to detail in data quality, governance, and documentation.
- Leadership and mentoring of junior analysts and BI developers.
Success Criteria
The successful candidate will:
- Deliver scalable, high-performance data pipelines and BI solutions using SQL, Python, PySpark, and cloud-native tools.
- Build governed semantic models and KPI frameworks that serve as a single source of truth across the organization.
- Prototype and deliver AI/LLM-powered analytics features that enhance insight discovery and decision-making.
- Enable self-service analytics capabilities for business users through well-governed BI platforms.
- Drive data quality, lineage, and governance standards across analytics assets.
- Mentor junior team members and establish BI and analytics best practices across the delivery team.
Job Description
BI Analyst / Senior Consultant – Business Intelligence & AI
Job Title
BI Analyst / Senior Consultant – BI & AI
Experience
6–9 Years
Location
Hybrid / Remote
Job Summary
We are seeking an experienced BI Analyst / Senior Consultant with 6–9 years of hands-on expertise in business intelligence, advanced analytics, and data engineering — combined with growing exposure to AI/LLM application development. The ideal candidate brings strong proficiency in SQL, Python, and PySpark for data processing, alongside deep BI platform expertise in Power BI or Tableau. They will have experience building governed semantic layers, developing scalable data pipelines, and integrating Large Language Model (LLM) capabilities into analytics workflows. This role sits at the intersection of traditional BI and next-generation AI-augmented analytics, making it ideal for a technically strong consultant ready to lead complex data initiatives.
Key Responsibilities
Data Engineering & Pipeline Development
- Write complex SQL queries, stored procedures, and optimized transformations across platforms such as Snowflake, Azure Synapse, BigQuery, or Redshift.
- Develop and maintain scalable data pipelines using Python and PySpark for large-scale batch and near-real-time data processing.
- Build and manage ELT/ETL workflows using dbt, ADF, Airflow, or Fivetran to ingest structured and semi-structured data.
- Implement Spark-based data processing on Databricks or Azure HDInsight for high-volume analytics workloads.
- Optimize query performance through partitioning, clustering, caching strategies, and execution plan analysis.
BI Development & Reporting
- Design, develop, and deploy enterprise-grade dashboards and reports using Power BI (DAX, Power Query, Composite Models) and Tableau.
- Build semantic models, calculated measures, KPI frameworks, and row-level security (RLS) configurations in Power BI or Looker.
- Develop LookML models, explores, and views in Looker to expose governed data layers for self-service analytics.
- Optimize BI report performance through DirectQuery tuning, aggregation tables, and incremental refresh strategies.
- Lead and mentor junior analysts in BI development standards, DAX best practices, and data modeling techniques.
Semantic Layer & Dimensional Modeling
- Design and maintain enterprise semantic models using dbt Semantic Layer, Power BI Semantic Models, Cube.dev, or AtScale.
- Build dimensional models (Star Schema, Snowflake Schema) with fact and dimension tables optimized for analytical query patterns.
- Define and standardize reusable business metrics, KPIs, hierarchies, and dimensions across reporting platforms.
- Ensure metric consistency and single source of truth across BI, dashboards, and AI-driven outputs.
AI & LLM Application Development (Exposure Required)
- Develop or contribute to AI-powered analytics applications using LLM APIs such as OpenAI GPT-4, Azure OpenAI, or Anthropic Claude.
- Build Retrieval-Augmented Generation (RAG) pipelines using frameworks such as LangChain or LlamaIndex to enable natural language querying over structured and unstructured data.
- Integrate LLM-generated insights, AI summaries, and conversational BI interfaces into existing Power BI or Tableau reporting workflows.
- Use Python libraries (openai, langchain, transformers, sentence-transformers) to prototype and deploy AI-driven analytics features.
- Implement vector search and embedding-based retrieval using tools such as FAISS, Pinecone, or Azure AI Search to surface contextual data insights.
- Contribute to prompt engineering, fine-tuning strategies, and evaluation frameworks for LLM outputs in analytics contexts.
- Explore and apply AI-native BI capabilities such as Power BI Copilot, Tableau Pulse, and Looker Explore AI.
Advanced Analytics & Data Science Integration
- Perform exploratory data analysis (EDA) using Python (pandas, numpy, matplotlib, seaborn, plotly) to surface trends and business insights.
- Collaborate with data science teams to integrate ML model outputs (e.g., churn scores, forecasts, classification results) into BI reporting layers.
- Develop statistical analyses, cohort analyses, and A/B test result reporting to support business experimentation.
- Apply time-series analysis and forecasting techniques using Python (statsmodels, Prophet, scikit-learn) for business planning use cases.
Stakeholder Engagement & Consulting
- Act as a senior analytical advisor to business stakeholders, translating complex data findings into clear business narratives.
- Lead requirement-gathering workshops, solution design sessions, and stakeholder demos for BI and AI analytics initiatives.
- Document functional and technical specifications for data pipelines, semantic models, and BI solutions.
- Participate in agile delivery — sprint planning, stand-ups, retrospectives — and manage delivery timelines for analytics workstreams.
Data Quality & Governance
- Implement data quality frameworks using dbt tests, Great Expectations, or custom SQL-based validation rules.
- Maintain data lineage, documentation, and metadata cataloging using tools such as Microsoft Purview, Alation, or dbt Docs.
- Define and enforce data governance standards, access control policies, and compliance requirements across BI and data assets.
Required Skills
Programming & Query Languages
- SQL — Advanced: CTEs, window functions, query optimization, stored procedures, dynamic SQL
- Python — Proficient: pandas, numpy, matplotlib, seaborn, sqlalchemy, requests, pyspark
- PySpark — Experience with distributed data processing, DataFrame API, Spark SQL, and UDFs
- DAX — Advanced: calculated columns, measures, time intelligence, row-level security
- LookML — Experience building models, explores, and views in Looker
- Shell scripting / Bash for pipeline automation and environment management
BI & Visualization Platforms
- Power BI — Advanced: Desktop, Service, Dataflows, Composite Models, Deployment Pipelines
- Tableau — Proficient: calculated fields, LOD expressions, Tableau Prep, Tableau Server
- Looker / LookML
- Sigma Computing or ThoughtSpot (preferred)
Data Platforms & Cloud
- Snowflake — Warehouses, clustering, materialized views, Snowpipe, dynamic data masking
- Azure: Azure Synapse Analytics, Azure Data Factory, Azure Databricks, Azure SQL
- AWS: Redshift, Glue, S3, Athena (preferred)
- GCP: BigQuery, Dataflow, Looker (preferred)
- Databricks — Delta Lake, Unity Catalog, MLflow (preferred)
Data Engineering & Integration Tools
- dbt (Core / Cloud) — models, tests, macros, seeds, snapshots, semantic layer
- Apache Airflow — DAG development, scheduling, operators
- Fivetran / Matillion / Azure Data Factory for data ingestion
- Apache Kafka or Azure Event Hubs for streaming data (preferred)
AI & LLM Technologies (Exposure Required)
- LLM APIs: OpenAI GPT-4 / GPT-4o, Azure OpenAI Service, Anthropic Claude
- LangChain or LlamaIndex for RAG pipeline development
- Vector databases: FAISS, Pinecone, Weaviate, or Azure AI Search
- Python AI libraries: openai, transformers, sentence-transformers, tiktoken
- Prompt engineering, context management, and chain-of-thought techniques
- Familiarity with AI-native BI tools: Power BI Copilot, Tableau Pulse, Looker Explore AI
- Microsoft Fabric or Azure AI Foundry exposure (preferred)
Semantic Layer Technologies
- dbt Semantic Layer / MetricFlow
- Power BI Semantic Models (Tabular / XMLA endpoint)
- Cube.dev or AtScale
- Snowflake Semantic Model (preferred)
DevOps & Delivery
- Git / GitHub / Azure DevOps — branching, pull requests, CI/CD pipelines
- Docker basics for containerized analytics environments
- Agile / Scrum delivery methodology
- JIRA / Azure Boards for sprint and backlog management
Preferred Qualifications
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
- Microsoft Certified: Power BI Data Analyst Associate (PL-300).
- Snowflake SnowPro Core or Advanced: Data Engineer Certification.
- Databricks Certified Associate Developer for Apache Spark.
- dbt Certified Developer (preferred).
- Experience in a consulting, professional services, or client-facing delivery environment.
- Hands-on experience building end-to-end AI/LLM-powered analytics applications.
Nice to Have
- Experience with Microsoft Fabric (OneLake, Fabric Notebooks, Real-Time Analytics).
- Exposure to MLOps practices: model versioning, monitoring, and deployment pipelines using MLflow or Azure ML.
- Knowledge of Data Vault 2.0 modeling methodology.
- Experience with real-time streaming analytics using Kafka, Spark Streaming, or Azure Stream Analytics.
- Familiarity with graph databases or knowledge graphs for AI-enhanced search.
- Exposure to data observability tools such as Monte Carlo, Anomalo, or dbt Artifacts.
Key Competencies
- Technical depth with the ability to move fluidly between SQL, Python, PySpark, and BI tooling.
- Strong analytical thinking and data-driven problem solving.
- Excellent communication skills — ability to present complex technical findings to non-technical stakeholders.
- Business acumen and senior stakeholder management in consulting environments.
- Curiosity and adaptability toward AI/LLM technologies and emerging analytics platforms.
- Collaborative mindset across data engineering, data science, and business teams.
- Attention to detail in data quality, governance, and documentation.
- Leadership and mentoring of junior analysts and BI developers.
Success Criteria
The successful candidate will:
- Deliver scalable, high-performance data pipelines and BI solutions using SQL, Python, PySpark, and cloud-native tools.
- Build governed semantic models and KPI frameworks that serve as a single source of truth across the organization.
- Prototype and deliver AI/LLM-powered analytics features that enhance insight discovery and decision-making.
- Enable self-service analytics capabilities for business users through well-governed BI platforms.
- Drive data quality, lineage, and governance standards across analytics assets.
- Mentor junior team members and establish BI and analytics best practices across the delivery team.
Job Description
BI Analyst / Senior Consultant – Business Intelligence & AI
Job Title
BI Analyst / Senior Consultant – BI & AI
Experience
6–9 Years
Location
Hybrid / Remote
Job Summary
We are seeking an experienced BI Analyst / Senior Consultant with 6–9 years of hands-on expertise in business intelligence, advanced analytics, and data engineering — combined with growing exposure to AI/LLM application development. The ideal candidate brings strong proficiency in SQL, Python, and PySpark for data processing, alongside deep BI platform expertise in Power BI or Tableau. They will have experience building governed semantic layers, developing scalable data pipelines, and integrating Large Language Model (LLM) capabilities into analytics workflows. This role sits at the intersection of traditional BI and next-generation AI-augmented analytics, making it ideal for a technically strong consultant ready to lead complex data initiatives.
Key Responsibilities
Data Engineering & Pipeline Development
- Write complex SQL queries, stored procedures, and optimized transformations across platforms such as Snowflake, Azure Synapse, BigQuery, or Redshift.
- Develop and maintain scalable data pipelines using Python and PySpark for large-scale batch and near-real-time data processing.
- Build and manage ELT/ETL workflows using dbt, ADF, Airflow, or Fivetran to ingest structured and semi-structured data.
- Implement Spark-based data processing on Databricks or Azure HDInsight for high-volume analytics workloads.
- Optimize query performance through partitioning, clustering, caching strategies, and execution plan analysis.
BI Development & Reporting
- Design, develop, and deploy enterprise-grade dashboards and reports using Power BI (DAX, Power Query, Composite Models) and Tableau.
- Build semantic models, calculated measures, KPI frameworks, and row-level security (RLS) configurations in Power BI or Looker.
- Develop LookML models, explores, and views in Looker to expose governed data layers for self-service analytics.
- Optimize BI report performance through DirectQuery tuning, aggregation tables, and incremental refresh strategies.
- Lead and mentor junior analysts in BI development standards, DAX best practices, and data modeling techniques.
Semantic Layer & Dimensional Modeling
- Design and maintain enterprise semantic models using dbt Semantic Layer, Power BI Semantic Models, Cube.dev, or AtScale.
- Build dimensional models (Star Schema, Snowflake Schema) with fact and dimension tables optimized for analytical query patterns.
- Define and standardize reusable business metrics, KPIs, hierarchies, and dimensions across reporting platforms.
- Ensure metric consistency and single source of truth across BI, dashboards, and AI-driven outputs.
AI & LLM Application Development (Exposure Required)
- Develop or contribute to AI-powered analytics applications using LLM APIs such as OpenAI GPT-4, Azure OpenAI, or Anthropic Claude.
- Build Retrieval-Augmented Generation (RAG) pipelines using frameworks such as LangChain or LlamaIndex to enable natural language querying over structured and unstructured data.
- Integrate LLM-generated insights, AI summaries, and conversational BI interfaces into existing Power BI or Tableau reporting workflows.
- Use Python libraries (openai, langchain, transformers, sentence-transformers) to prototype and deploy AI-driven analytics features.
- Implement vector search and embedding-based retrieval using tools such as FAISS, Pinecone, or Azure AI Search to surface contextual data insights.
- Contribute to prompt engineering, fine-tuning strategies, and evaluation frameworks for LLM outputs in analytics contexts.
- Explore and apply AI-native BI capabilities such as Power BI Copilot, Tableau Pulse, and Looker Explore AI.
Advanced Analytics & Data Science Integration
- Perform exploratory data analysis (EDA) using Python (pandas, numpy, matplotlib, seaborn, plotly) to surface trends and business insights.
- Collaborate with data science teams to integrate ML model outputs (e.g., churn scores, forecasts, classification results) into BI reporting layers.
- Develop statistical analyses, cohort analyses, and A/B test result reporting to support business experimentation.
- Apply time-series analysis and forecasting techniques using Python (statsmodels, Prophet, scikit-learn) for business planning use cases.
Stakeholder Engagement & Consulting
- Act as a senior analytical advisor to business stakeholders, translating complex data findings into clear business narratives.
- Lead requirement-gathering workshops, solution design sessions, and stakeholder demos for BI and AI analytics initiatives.
- Document functional and technical specifications for data pipelines, semantic models, and BI solutions.
- Participate in agile delivery — sprint planning, stand-ups, retrospectives — and manage delivery timelines for analytics workstreams.
Data Quality & Governance
- Implement data quality frameworks using dbt tests, Great Expectations, or custom SQL-based validation rules.
- Maintain data lineage, documentation, and metadata cataloging using tools such as Microsoft Purview, Alation, or dbt Docs.
- Define and enforce data governance standards, access control policies, and compliance requirements across BI and data assets.
Required Skills
Programming & Query Languages
- SQL — Advanced: CTEs, window functions, query optimization, stored procedures, dynamic SQL
- Python — Proficient: pandas, numpy, matplotlib, seaborn, sqlalchemy, requests, pyspark
- PySpark — Experience with distributed data processing, DataFrame API, Spark SQL, and UDFs
- DAX — Advanced: calculated columns, measures, time intelligence, row-level security
- LookML — Experience building models, explores, and views in Looker
- Shell scripting / Bash for pipeline automation and environment management
BI & Visualization Platforms
- Power BI — Advanced: Desktop, Service, Dataflows, Composite Models, Deployment Pipelines
- Tableau — Proficient: calculated fields, LOD expressions, Tableau Prep, Tableau Server
- Looker / LookML
- Sigma Computing or ThoughtSpot (preferred)
Data Platforms & Cloud
- Snowflake — Warehouses, clustering, materialized views, Snowpipe, dynamic data masking
- Azure: Azure Synapse Analytics, Azure Data Factory, Azure Databricks, Azure SQL
- AWS: Redshift, Glue, S3, Athena (preferred)
- GCP: BigQuery, Dataflow, Looker (preferred)
- Databricks — Delta Lake, Unity Catalog, MLflow (preferred)
Data Engineering & Integration Tools
- dbt (Core / Cloud) — models, tests, macros, seeds, snapshots, semantic layer
- Apache Airflow — DAG development, scheduling, operators
- Fivetran / Matillion / Azure Data Factory for data ingestion
- Apache Kafka or Azure Event Hubs for streaming data (preferred)
AI & LLM Technologies (Exposure Required)
- LLM APIs: OpenAI GPT-4 / GPT-4o, Azure OpenAI Service, Anthropic Claude
- LangChain or LlamaIndex for RAG pipeline development
- Vector databases: FAISS, Pinecone, Weaviate, or Azure AI Search
- Python AI libraries: openai, transformers, sentence-transformers, tiktoken
- Prompt engineering, context management, and chain-of-thought techniques
- Familiarity with AI-native BI tools: Power BI Copilot, Tableau Pulse, Looker Explore AI
- Microsoft Fabric or Azure AI Foundry exposure (preferred)
Semantic Layer Technologies
- dbt Semantic Layer / MetricFlow
- Power BI Semantic Models (Tabular / XMLA endpoint)
- Cube.dev or AtScale
- Snowflake Semantic Model (preferred)
DevOps & Delivery
- Git / GitHub / Azure DevOps — branching, pull requests, CI/CD pipelines
- Docker basics for containerized analytics environments
- Agile / Scrum delivery methodology
- JIRA / Azure Boards for sprint and backlog management
Preferred Qualifications
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
- Microsoft Certified: Power BI Data Analyst Associate (PL-300).
- Snowflake SnowPro Core or Advanced: Data Engineer Certification.
- Databricks Certified Associate Developer for Apache Spark.
- dbt Certified Developer (preferred).
- Experience in a consulting, professional services, or client-facing delivery environment.
- Hands-on experience building end-to-end AI/LLM-powered analytics applications.
Nice to Have
- Experience with Microsoft Fabric (OneLake, Fabric Notebooks, Real-Time Analytics).
- Exposure to MLOps practices: model versioning, monitoring, and deployment pipelines using MLflow or Azure ML.
- Knowledge of Data Vault 2.0 modeling methodology.
- Experience with real-time streaming analytics using Kafka, Spark Streaming, or Azure Stream Analytics.
- Familiarity with graph databases or knowledge graphs for AI-enhanced search.
- Exposure to data observability tools such as Monte Carlo, Anomalo, or dbt Artifacts.
Key Competencies
- Technical depth with the ability to move fluidly between SQL, Python, PySpark, and BI tooling.
- Strong analytical thinking and data-driven problem solving.
- Excellent communication skills — ability to present complex technical findings to non-technical stakeholders.
- Business acumen and senior stakeholder management in consulting environments.
- Curiosity and adaptability toward AI/LLM technologies and emerging analytics platforms.
- Collaborative mindset across data engineering, data science, and business teams.
- Attention to detail in data quality, governance, and documentation.
- Leadership and mentoring of junior analysts and BI developers.
Success Criteria
The successful candidate will:
- Deliver scalable, high-performance data pipelines and BI solutions using SQL, Python, PySpark, and cloud-native tools.
- Build governed semantic models and KPI frameworks that serve as a single source of truth across the organization.
- Prototype and deliver AI/LLM-powered analytics features that enhance insight discovery and decision-making.
- Enable self-service analytics capabilities for business users through well-governed BI platforms.
- Drive data quality, lineage, and governance standards across analytics assets.
- Mentor junior team members and establish BI and analytics best practices across the delivery team.