Engineer - Data Engineering & Analytics
Millennium IT ESP Colombo, Western Province, Sri Lanka
IT Services and IT Consulting · 201-500 employees
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About the role
Implement and optimize real-time ETL/ELT pipelines and data workflows on enterprise platforms to ensure high performance and reliability. Collaborate with client stakeholders throughout the delivery lifecycle, including requirements gathering, data model validation, and post-deployment support.
What they look for
Requirements
Requires a Bachelor's or Master's degree in Data Science, Data Engineering, or AI, along with 2-3 years of professional experience in building enterprise data pipelines. Candidates must demonstrate hands-on experience with platforms like Databricks or Snowflake and possess strong skills in SQL, Python, and distributed data processing.
Full description
Job Description
- Implement real-time ETL/ELT pipelines on enterprise data platforms, including Databricks, Snowflake, Microsoft Fabric, Cloudera, Informatica IDMC, or Oracle
- Implement data workflows using technologies such as 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 one or more technologies, including Oracle GoldenGate, Apache Kafka, and Spark Structured Streaming
- Apply dimensional data modelling, including Kimball star schemas, to deliver analytics-ready data marts
- Implement data governance, security, data quality, and lineage using Databricks Unity Catalog, Microsoft Purview, Cloudera SDX, and Informatica Data Quality
- Apply DataOps practices, including Git-based version control, CI/CD for data pipelines, automated testing, and Infrastructure as Code
- Monitor, troubleshoot, and optimise production pipelines for performance and cloud cost efficiency, supporting the practice's 99.90% uptime SLA commitment
- Work directly with client stakeholders throughout the delivery lifecycle, including requirements gathering, data model validation, User Acceptance Testing (UAT), Go-Live, and post-Go-Live SLA support
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 recognised university
- Have 2–3 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
- 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, including Informatica PowerCenter, SSIS, or ODI, or on-premises data warehouses to modern cloud lakehouse platforms will be considered an added advantage
- Strong SQL and Python (PySpark) skills, along with knowledge of Scala or Java, will be considered an added advantage
