Data Engineering Manager (Azure, Databricks)

What You Will Do 

• Own the end-to-end architecture of the centralized data platform: batch and streaming ingestion, lakehouse/warehouse modeling, data serving, and orchestration 

• Lead, mentor, and grow a team of 4–8 data engineers; set code review, testing, and documentation standards 

• Design and drive the consolidation roadmap: migrating BU-level pipelines (retail POS, supply chain, loyalty, e-commerce, FMCG distribution) into unified, governed data domains 

• Build for scale and reliability: petabyte-scale datasets, near-real-time pipelines for store operations and loyalty, SLAs for downstream analytics and ML workloads 

• Establish data quality, lineage, and observability practices across the platform 

• Partner with the AI/ML platform team to serve feature-ready, ML-consumable data 

• Evaluate and own build-vs-buy decisions across the modern data stack; manage cloud cost efficiency at scale 

Must-Have 

• 7+ years in data engineering, with 2+ years leading engineers or owning platform architecture as a tech lead 

• Deep, hands-on expertise with distributed data processing (Spark, Flink, or equivalent) and streaming (Kafka) 

• Strong experience with lakehouse/warehouse architectures (Databricks, Snowflake, BigQuery, or open-source equivalents — Delta/Iceberg/Hudi) 

• Production experience on a major cloud (AWS/GCP/Azure) including cost and performance optimization at large data volumes 

• Strong SQL and Python (or Scala/Java); solid data modeling fundamentals (dimensional, Data Vault, or domain-oriented) 

• Experience with orchestration (Airflow/Dagster), CI/CD for data, and infrastructure-as-code 

• Track record of leading data platform consolidation, migration, or greenfield builds in high-volume environments (retail, e-commerce, fintech, telco preferred) 

• Able to communicate architecture and trade-offs to non-technical stakeholders; English working proficiency 

Nice to have 

• Data mesh / data domain operating models at multi-BU scale 

• Real-time analytics serving (ClickHouse, Druid, Pinot) 

• Data governance tooling (catalog, lineage — DataHub, OpenMetadata) 

• Experience supporting ML feature platforms 

 

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