Engineering Manager – Enterprise Data Engineering

Description

The company is a subsidiary of a larger parent group and makes gaming hardware and online network services used by more than 100 million people. It also creates entertainment and some of the best-known intellectual properties in the world.

This team builds trusted, scalable and reusable data capabilities for the company's enterprise functions. The Engineering Manager leads a group of enterprise data engineers and owns people leadership, technical direction, delivery and operational accountability for data products, pipelines and integrations.

You will work with global engineering, architecture, platform, security and business teams on priorities in Finance, Supply Chain, IT Operations, People & Places, and Sales & Operations. The role balances new transformation work with keeping existing data services reliable, maintainable and improving.

Responsibilities

• Lead, coach and grow the engineering team. Handle performance reviews, development, hiring and succession, and set clear goals, regular feedback and real career paths.
• Build a team culture based on ownership, inclusion, engineering discipline, collaboration and ongoing learning.
• Step into hands-on technical work when needed, while handing decisions to engineers and growing senior technical capability.
• Turn enterprise roadmaps into workable plans with milestones, dependencies and measurable outcomes, balancing project work against run-the-business duties.
• Forecast capacity and skills needs in advance, allocate people, manage competing priorities and point out delivery trade-offs.
• Keep progress, blockers and risks visible. Raise issues early with options and a recommendation, and speak clearly to leadership and stakeholders.
• Make delivery predictable through suitable agile practices, estimation, design reviews, testing, release management and continuous improvement.
• Guide the architecture and build of secure, scalable batch and streaming ingestion, ETL/ELT pipelines, transformations and enterprise integrations.
• Set maintainable engineering patterns across Snowflake, Databricks, Python/PySpark, SQL, dbt and related orchestration and integration tools.
• Make sure data models and domain-owned data products are reusable, discoverable, documented and consistent with enterprise standards.
• Put source control, peer review, automated testing, CI/CD, infrastructure-as-code where suitable, and sound software engineering practice in place.
• Own reliability, observability, data freshness, data quality, lineage, access controls and support for the team's pipelines and products.
• Set up monitoring, alerting, incident response, root-cause analysis and recovery procedures, and cut the support effort that keeps recurring.
• Work with platform, architecture, security and governance teams on production readiness, privacy, compliance, cost and performance.
• Work with domain and transformation leaders to understand business goals, shape requirements and choose data engineering solutions that last.
• Coordinate dependencies with data platform, analytics, AI/ML, application and infrastructure teams so work moves end to end.
• Present the team's architecture, products, delivery status, risks and impact to technical and executive audiences.

Requirements

• You have 3+ years managing people or leading engineering, including performance management, coaching and leading delivery teams.
• You have 8+ years of relevant software, data platform or data engineering work, with clear ownership of production-grade enterprise data solutions.
• You understand data architecture, ETL/ELT, warehousing and lakehouse patterns, dimensional modelling, data contracts and reusable data products.
• You have hands-on experience with SQL and Python, and you know Snowflake, Databricks and PySpark well. You have used dbt or a comparable transformation framework.
• You have worked with orchestration, API and file-based integrations, cloud services (preferably AWS), Git-based development, automated testing and CI/CD.
• You have run business-critical data pipelines, covering monitoring, incident management, data quality, security and performance tuning.
• You can plan team capacity, manage a portfolio of priorities, resolve cross-team dependencies and report delivery progress openly and proactively.
• You communicate well and manage stakeholders across engineering, business and senior leadership. You hold an undergraduate degree or equivalent experience.
• Desirable: Experience with enterprise Finance or Supply Chain data, including SAP and planning-system integrations such as Anaplan or Adaptive.
• Desirable: Experience with data governance, metadata and lineage, master data, privacy classification and regulated or sensitive enterprise datasets.
• Desirable: Experience building analytics, machine learning or AI-ready data products and using AI-assisted engineering practices responsibly.
• Desirable: Experience working with geographically distributed teams and large enterprise transformation programmes.

Conditions

The company runs background checks at the offer stage for all new hires, which may include criminal background checks for some roles, and will need to process personal information for them.