Data Scientist

Key Responsibilities

•Design, develop and maintain scalable ETL/ELT data pipelines for large-volume structured and unstructured datasets.

•Develop high-performance data processing solutions using Python, PySpark, Apache Spark, Spark SQL and Scala.

•Build batch and real-time data pipelines using Kafka, Kinesis, Spark Streaming, AWS Glue and Airflow.

•Develop data ingestion frameworks integrating RDBMS, APIs, files, cloud storage and streaming platforms.

•Design and implement data lakes, data warehouses and cloud-based data processing platforms.

•Work with Databricks, Hadoop, Hive, Cloudera, Presto and Snowflake for large-scale data processing and analytics.

•Perform data modelling, data transformation, data quality, query optimisation and performance tuning.

•Develop and optimise SQL solutions across Oracle, SQL Server, PostgreSQL, Teradata, MongoDB and cloud databases.

•Design and implement data migration solutions involving large-scale enterprise datasets.

•Develop and support real-time and batch processing architectures for enterprise applications.

•Integrate data platforms with REST APIs, GraphQL and enterprise applications.

•Implement CI/CD and DevOps practices using Jenkins, Git, Docker, Kubernetes and OpenShift.

•Develop cloud-native data solutions using AWS and Azure, including S3, Glue, EMR, Redshift, Kinesis, Lambda, RDS and DynamoDB.

•Develop AI/GenAI-enabled data solutions involving LLMs, NLP, RAG, Agentic AI and vector databases.

•Integrate LLM services and AI platforms such as Azure OpenAI, OpenAI APIs, Hugging Face and Google Gemini/ADK.

•Develop NLP pipelines for text processing, embeddings, summarisation, sentiment analysis, voice-to-text and speaker diarisation.

•Design and implement vector search and retrieval solutions using Redis, ChromaDB and FAISS.

•Develop AI-powered APIs and applications using FastAPI, Gradio and Python.

•Collaborate with architects, data scientists, software engineers, business analysts and product teams to deliver enterprise data solutions.

•Participate in Agile SDLC activities including requirements analysis, architecture, development, testing, deployment and production support.

•Troubleshoot complex data, application and platform issues and provide scalable technical solutions.

Required Technical Skills

Data Engineering:

Python, PySpark, Apache Spark, Spark SQL, Scala, Hadoop, Hive, Kafka, Presto, Databricks, Cloudera, Snowflake

Cloud Technologies:

AWS, Azure, S3, Glue, EMR, Redshift, Kinesis, Lambda, RDS, DynamoDB, OpenSearch

Databases:

SQL Server, Oracle, PostgreSQL, Teradata, MongoDB, Redis

Programming:

Python, Java, Scala, SQL, Shell Scripting, Node.js

AI / GenAI / NLP:

Generative AI, LLM, NLP, RAG, Agentic RAG, LangChain, LangGraph, LlamaIndex, Hugging Face Transformers, Azure OpenAI, OpenAI API, Google Gemini/ADK, PyTorch

Vector & AI Search:

Redis Vector Database, ChromaDB, FAISS, Embeddings, Hybrid Search, Semantic Search

DevOps & Deployment:

Docker, Kubernetes, OpenShift, Jenkins, Git, Terraform, CI/CD

Data Integration & APIs:

REST APIs, GraphQL, FastAPI, API Gateway, AWS Lambda, CDC, Debezium

Data Visualisation:

Power BI, Data Modelling, Reporting and Analytics

Qualifications

•Bachelor's or Master's degree in Computer Science, Information Technology, Programming & Systems Analysis, Computer Studies or a related discipline.

•Strong professional experience in Big Data, Cloud Computing or related technology domains.

•Experience working with large-scale enterprise data platforms and production data pipelines.

•Strong programming and SQL skills.

•Experience with cloud-based data engineering and modern data processing frameworks.

•Experience with AI/ML, NLP or Generative AI will be highly advantageous.

Preferred Experience

•Enterprise Banking / Financial Services experience.

•Experience working with large-scale customer, transaction or financial datasets.

•Experience with data migration and legacy ETL modernisation.

•Experience implementing AI/GenAI solutions within enterprise data platforms.

•Experience with production deployments and CI/CD environments.

•Strong understanding of data governance, security, data quality and performance optimisation.