Engineering Manager (Enterprise Data Engineering)

PlayStation; Ireland; On-site; EUR 120,000 - 180,000; Full time

Job summary

PlayStation is seeking an Engineering Manager to lead a team of Enterprise Data Engineers delivering trusted, scalable data capabilities across the organization. You will drive technical direction, delivery ownership and operational accountability for data products, pipelines and integrations.

You will collaborate with global teams across Finance, Supply Chain, IT Operations, People & Places and Sales & Operations to enable enterprise priorities while maintaining reliability and continuous

Qualifications

• 8+ years in software/data engineering with production ownership.
• 3+ years in people management or engineering leadership.
• Experience with enterprise data platforms and pipelines.
• Strong stakeholder management and communication.
• Hands-on with SQL and Python.

Responsibilities

• Lead and grow a team of data engineers.
• Translate roadmaps into executable plans.
• Oversee delivery, quality, and reliability of data products.
• Partner with architecture, security and governance teams.
• Ensure production readiness and incident management.

Skills

Team leadership

People management

Agile delivery

Stakeholder communication

Technical guidance

Capacity planning

Data engineering

Mentoring

Education

Bachelor's degree in a technical field

Engineering leadership experience

Tools

Snowflake

Databricks

Python/PySpark

dbt

AWS

Job description

• 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
• 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
• 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
• 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
• 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
• 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

• Ability to plan team capacity, manage a portfolio of priorities, resolve cross‑team dependencies and provide transparent, proactive delivery reporting
• Practical technical experience with SQL and Python; strong familiarity with Snowflake, Databricks and PySpark, and experience with dbt or comparable transformation frameworks
• Strong understanding of data architecture, ETL/ELT, data warehousing/lakehouse patterns, dimensional modelling, data contracts and reusable data products
• 8+ years of relevant software, data platform or data engineering experience, with demonstrated ownership of production‑grade enterprise data solutions
• 3+ years of people management or engineering leadership experience, including performance management, coaching and leading delivery teams
• Experience operating business‑critical data pipelines, including monitoring, incident management, data quality, security and performance optimisation
• Experience with orchestration, API/file‑based integrations, cloud services (preferably AWS), Git‑based development, automated testing and CI/CD
• Excellent stakeholder management and communication skills across engineering, business and senior leadership; undergraduate degree or equivalent experience
• Enterprise Finance or Supply Chain data, including SAP and planning‑system integrations (such as Anaplan or Adaptive)
• Enabling analytics, machine learning or AI‑ready data products and applying AI‑assisted engineering practices responsibly
• Data governance, metadata/lineage, master data, privacy classification and regulated or sensitive enterprise datasets
• Working with geographically distributed teams and large‑scale enterprise transformation programmes