Senior Engineer – Data Engineering & Analytics

Job Description Design, build, and maintain scalable batch and real-time ETL/ELT pipelines on enterprise data platforms, including Databricks, Snowflake, Microsoft Fabric, Cloudera, Informatica IDMC, and Oracle Develop lakehouse and data warehouse solutions using Medallion (Bronze/Silver/Gold) architecture on Delta Lake, Apache Iceberg, OneLake, Snowflake, and Oracle Autonomous Data Warehouse (ADW) Build and orchestrate data workflows using Databricks Lakeflow, Fabric Data Factory, Azure Data Factory, Informatica Cloud Data Integration, Snowflake Streams & Tasks, and Apache Airflow Implement Change Data Capture (CDC) and streaming ingestion using Oracle GoldenGate, Apache Kafka, and Spark Structured Streaming Apply dimensional data modelling, including Kimball star schemas, to deliver analytics-ready data marts Develop Power BI semantic models, including Direct Lake, DAX, and row-level security, in partnership with BI and analytics teams Implement data governance, security, data quality, and lineage using Databricks Unity Catalog, Microsoft Purview, Cloudera SDX, and Informatica Data Quality Prepare governed, high-quality data for AI and Machine Learning use cases, including feature pipelines and RAG-ready datasets using vector search capabilities on Databricks, Snowflake Cortex, and Oracle AI Vector Search Apply DataOps practices, including Git-based version control, CI/CD for data pipelines, automated testing, and Infrastructure as Code Monitor, troubleshoot, and optimize production pipelines for performance and cloud cost, supporting the practice's 99.90% uptime SLA commitment Work directly with client stakeholders across the delivery lifecycle, including requirements gathering, data model validation, UAT, Go-Live, and post-Go-Live SLA support Mentor junior engineers and contribute to internal engineering standards, reusable pipeline frameworks, and technical documentation Person Specification Possess a Bachelor's Degree in Data Science or a higher qualification, such as an MSc in Data Science, Data Engineering, or Artificial Intelligence, from a recognized university Have 2–5 years of professional experience in building and operating enterprise data pipelines, data warehouses, or lakehouses Possess hands-on experience with at least two of the following platforms: Databricks, Snowflake, Microsoft Fabric/Azure Data Services, Cloudera, Informatica (IDMC/PowerCenter), or Oracle (ADW/Exadata/ODI) Demonstrate strong experience with Apache Spark and...

Summary and match are AI-generated; verify details on the original posting.

Listed via Himalayas. All sources