Engineering Manager- Data & AI

Role Overview

We are looking for an experienced Data Engineering Manager to lead, mentor, and scale a engineering team responsible for building robust data platforms, data pipelines, and downstream analytics applications. You will lead end-to-end delivery of data engineering, analytics, and data platform projects, working closely with business stakeholders, data engineers, architects, and product teams to translate complex business needs into technical deliverables.

This role balances team management with technical leadership: you will manage people, guide solution design, review code and pipeline architectures, challenge engineering estimates, and hold your own in technical discussions. Experience in building data apps, data warehouses, semantic models, and data integration layers is required.

Key Responsibilities

• 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.

Technical Knowledge / Experience Required

• 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 modeling, 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.