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We are hiring a T-shaped Senior Data Engineer to join our team supporting a high-impact engagement with a large US-based client. The client is modernizing its sprawling legacy MSSQL data reporting ecosystem and migrating to a modern Databricks Lakehouse on Azure (target stack includes Databricks, Delta Live Tables, dbt, and strong data governance). As a T-shaped engineer, you’ll bring deep expertise in Databricks and data platform modernization, combined with broad knowledge across the data engineering landscape. You’ll design and build scalable, reliable data solutions while working closely with US stakeholders in a client-facing capacity. This is a hands-on role involving legacy migration (including stored procedures), building modern pipelines, and helping drive the full data stack transformation.
Job Responsibility
Lead the design, development, and optimisation of data pipelines using Databricks (including Delta Live Tables / Lakeflow) on Azure
Migrate and refactor legacy stored procedures and sprawling MSSQL processes into modern, governed Databricks solutions
Contribute to data platform modernisation initiatives, including medallion architecture, data quality, and governance practices
Collaborate with the client’s team and our Analytics Engineers to deliver high-quality, production-ready data assets
Work in a client-facing consulting environment: gather requirements, provide recommendations, and professionally push back when needed to ensure the best technical outcomes
Apply broad data engineering skills (pipelining, orchestration, cloud infrastructure) while deepening your expertise in the modern data stack
Requirements
5+ years of experience
Programming languages: Python, Java, Scala or Golang
Cloud providers: AWS, GCP, Azure (Azure is preferred)
Data warehousing: Snowflake, Databricks, Redshift, Greenplum, Oracle, DB2 or similar (must have experience with Snowflake or Databricks)
Data pipelining tools: Snowplow, Fivetran, dbt, Talend
Relational databases: Oracle, PostgreSQL, MySQL, Microsoft or similar
NoSQL Databases: MongoDB (Atlas), Cassandra, DynamoDB or similar
Streaming Technologies: Kinesis, Kafka or Google PubSub or similar