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

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

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

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

Engineer - Data Engineering & Analytics

Skills