数据工程与分析高级工程师

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

Originally posted on Himalayas