The incumbent will design, build, and maintain scalable data solutions that support analytics, machine learning, and business insights. Responsibilities include developing robust data pipelines, cloud-based data platforms, analytics solutions, and supporting AI/ML initiatives while ensuring data quality, governance, and operational excellence.
Responsibilities
Design, develop, and maintain ETL/ELT pipelines across cloud and on-premise environments, integrating internal and external data sources, including compliant ingestion of public data where required.
Build, optimize, and maintain scalable data models (e.g., star and snowflake schemas) to support analytics, reporting, and business intelligence requirements.
Ensure data quality, integrity, and availability through validation, cleansing, transformation processes, and implementation of monitoring controls.
Implement and manage cloud-based data solutions on Microsoft Azure, including Azure Data Factory, Data Lake Storage, Azure SQL, and related services.
Support infrastructure configuration and administration, including compute, storage, networking, identity management, and security controls to ensure reliable and scalable platform operations.
Implement CI/CD pipelines, automate deployments, and establish monitoring, logging, and alerting capabilities to maintain performance, reliability, and SLA adherence.
Support the development and optimization of interactive dashboards and reports using Power BI, ensuring seamless integration between data pipelines and reporting layers.
Collaborate with business stakeholders to understand requirements and translate them into effective data solutions and visualizations.
Collaborate with Data Scientists to deploy, support, and monitor machine learning models in production environments and integrate AI/ML capabilities into data pipelines and applications.
Ensure adherence to data governance, security, compliance, access control, and data lineage requirements while following software engineering best practices such as version control, code reviews, and modular design.
Contribute to continuous improvement of data platform standards, operational performance, scalability, and cost optimization initiatives.
Requirements
Possess a bachelor's degree in Computer Science, Computer Engineering, or a related field.
2-3 years of experience in data engineering, software engineering, or related roles with exposure to data platforms, cloud technologies, and analytics solutions.
Strong analytical thinking, problem-solving, and troubleshooting abilities.
Good communication and collaboration skills with the ability to work effectively across technical and business teams.
Self-motivated, detail-oriented, and able to manage multiple priorities in a dynamic environment.
Possess initiative and willingness to learn new technologies, tools, and methodologies.
Technical Skills Required
Experience with data warehousing concepts, data modeling, database optimization techniques, and ETL/ELT development using Python or ETL tools (e.g., SSIS, Informatica).
Proficiency in relational database technologies such as Microsoft SQL Server and Oracle, and familiarity with big data technologies such as Spark and Databricks.
Proficiency in Python for data processing, data engineering tasks, and basic API development.
Hands-on experience with Microsoft Azure services, including Azure Data Factory, Data Lake Storage, and Azure SQL.
Familiarity with CI/CD pipelines, DevOps practices, and automation tools such as Azure DevOps.
Familiarity with streaming or real-time data processing concepts and exposure to monitoring, performance tuning, and optimization of data systems.
Experience with Power BI, including DAX and Power Query, for dashboard development and reporting.
Basic understanding of machine learning workflows and tools, including Scikit-learn and Azure Machine Learning.
Only shortlisted candidates will be notified.