Data Scientist

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Description

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

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