Data Engineering Manager - Data & Analytics
Description
- Key Skills: Python (Strong), Advanced SQL, Airflow/Spark/Kafka, Cloud (AWS/Azure/GCP), Modern DW (Snowflake/Databricks), Data Pipeline ETL/ELT.
- Core Domains: Proven experience building data applications, data warehouses, semantic models, and data integration layers.
- Programming & Workflows: Strong familiarity with Python (or SQL-based workflows) to review pipeline logic and engage in technical code reviews.
- Data Fundamentals: Core ETL/ELT pipelines, data modelling, data quality, and governance.
- Cloud & Databases: Hands-on familiarity with cloud data platforms (Azure, AWS, or GCP) along with relational and non-relational database systems.
- Team Leadership & Development: Mentor and manage a team of data engineers, driving technical excellence and accountability.
- End-to-End Delivery: Lead the delivery of data engineering, analytics, and platform initiatives from design to production.
- Architecture Oversight: Guide the implementation of scalable data integration layers, enterprise data warehouses, semantic models, and analytics applications.
- Technical Oversight: Review pipeline designs, challenge engineering estimates, and drive architecture discussions.
- Project & Scope Governance: Own project scope, timelines, budgets, risks, and dependencies; proactively mitigate execution risks.
- Cross-Functional Collaboration: Partner with Data Scientists, Architects, Product Managers, and Business Analysts to translate business goals into technical deliverables.
- Sprint & Status Execution: Drive sprint planning, status tracking, stakeholder communication, and senior leadership reporting.
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