Engineering Manager – Enterprise Data Engineering

Engineering Manager – Enterprise Data Engineering

We are looking for an experienced Engineering Manager to lead a team of Enterprise Data Engineers delivering trusted, scalable and reusable data capabilities across Sony Interactive Entertainment (SIE). The role combines people leadership, technical direction, delivery ownership and operational accountability for enterprise data products, pipelines and integrations.

Working with global engineering, architecture, platform, security and business teams, you will enable enterprise priorities spanning Finance, Supply Chain, IT Operations, People & Places and Sales & Operations. You will balance transformation delivery with the reliability, maintainability and continuous improvement of existing data services.

Responsibilities

Lead, coach and grow the engineering team

· Manage performance, development, recruitment and succession; create clear goals, regular feedback and meaningful career plans.

· Build a culture of ownership, inclusion, engineering discipline, collaboration and continuous learning.

· Provide hands-on technical guidance when needed while delegating decisions and developing senior engineering capability.

Own delivery, capacity and execution

· Translate enterprise roadmaps into executable plans, milestones, dependencies and measurable outcomes; balance project commitments with run-the-business obligations.

· Proactively forecast capacity and skills needs, allocate resources, manage competing priorities and identify delivery trade-offs.

· Maintain clear visibility of progress, blockers and risks; escalate early with options and recommendations, and communicate confidently to leadership and stakeholders.

· Drive predictable delivery through appropriate agile practices, estimation, design reviews, testing, release management and continuous improvement.

Provide technical leadership for enterprise data engineering

· Guide architecture and implementation of secure, scalable batch and streaming ingestion, ETL/ELT pipelines, transformations and enterprise integrations.

· Champion maintainable engineering patterns across Snowflake, Databricks, Python/PySpark, SQL, dbt and relevant orchestration and integration technologies.

· Ensure data models and domain-owned data products are reusable, discoverable, documented and aligned with enterprise standards.

· Embed source control, peer review, automated testing, CI/CD, infrastructure-as-code where appropriate, and sound software engineering practices.

Own operational quality and production readiness

· Be accountable for service reliability, observability, data freshness, data quality, lineage, access controls and operational support for team-owned pipelines and products.

· Establish effective monitoring, alerting, incident response, root-cause analysis, recovery procedures and reduction of recurring support effort.

· Partner with platform, architecture, security and governance teams on production readiness, privacy, compliance, cost and performance optimisation.

Partner across the enterprise

· Work with domain and transformation leaders to understand business outcomes, shape requirements and prioritise sustainable data engineering solutions.

· Coordinate dependencies with data platform, analytics, AI/ML, application and infrastructure teams to enable end-to-end delivery.

· Represent the team’s architecture, products, delivery status, risks and impact in technical and executive forums.

Skills and Qualifications

· 3+ years of people management or engineering leadership experience, including performance management, coaching and leading delivery teams.

· 8+ years of relevant software, data platform or data engineering experience, with demonstrated ownership of production-grade enterprise data solutions.

· Strong understanding of data architecture, ETL/ELT, data warehousing/lakehouse patterns, dimensional modelling, data contracts and reusable data products.

· Practical technical experience with SQL and Python; strong familiarity with Snowflake, Databricks and PySpark, and experience with dbt or comparable transformation frameworks.

· Experience with orchestration, API/file-based integrations, cloud services (preferably AWS), Git-based development, automated testing and CI/CD.

· Experience operating business-critical data pipelines, including monitoring, incident management, data quality, security and performance optimisation.

· Ability to plan team capacity, manage a portfolio of priorities, resolve cross-team dependencies and provide transparent, proactive delivery reporting.

· Excellent stakeholder management and communication skills across engineering, business and senior leadership; undergraduate degree or equivalent experience.

Desirable Experience

·  Enterprise Finance or Supply Chain data, including SAP and planning-system integrations (such as Anaplan or Adaptive).

· Data governance, metadata/lineage, master data, privacy classification and regulated or sensitive enterprise datasets.

· Enabling analytics, machine learning or AI-ready data products and applying AI-assisted engineering practices responsibly.

· Working with geographically distributed teams and large-scale enterprise transformation programmes.

 

Salary: €101,000 - €151,400