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. #J-18808-Ljbffr