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 distributed data processing at scale
- Possess a solid understanding of data modelling, data quality, and data governance principles
- Have experience developing Power BI semantic models and reports
- Demonstrate strong communication skills and the ability to work directly with client stakeholders
- Professional certifications such as Databricks Certified Data Engineer (Associate/Professional), SnowPro Core or SnowPro Advanced: Data Engineer, Microsoft Certified: Fabric Data Engineer Associate (DP-700) or Fabric Analytics Engineer Associate (DP-600), Informatica IDMC, or Oracle Autonomous Database certifications will be considered an added advantage
- Experience in migrating legacy ETL platforms such as Informatica PowerCenter, SSIS, or ODI, or on-premises data warehouses to modern cloud lakehouse platforms will be considered an added advantage
- Experience with real-time streaming and CDC tools, including Kafka and Oracle GoldenGate, will be considered an added advantage
- Exposure to GenAI data engineering, including RAG pipelines, vector databases, and LLM-ready data preparation, will be considered an added advantage
- Experience with dbt, Terraform, Azure DevOps, or GitHub Actions will be considered an added advantage
- Prior experience in banking, telecommunications, or public-sector data projects will be considered an added advantage
- Demonstrate strong SQL and Python (PySpark) skills; knowledge of Scala or Java will be considered an added advantage
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