Senior Data Engineer

PlayStation; Ireland; On-site; EUR 90,000 - 130,000; Full time

Job summary

PlayStation seeks an experienced Data Engineer to design, build and operate robust batch and near-real-time data pipelines across enterprise systems, APIs and cloud environments.

You will own end-to-end data solutions, mentor peers, and collaborate with security, governance and analytics teams to deliver trusted data for analytics and AI projects.

Qualifications

• Strong hands-on SQL and Python skills.
• 6+ years of professional data engineering or software engineering experience.
• Experience designing data pipelines with modern ETL/ELT tools.
• Experience with orchestration and cloud data platforms (Snowflake, AWS).
• Knowledge of data governance, security and quality practices.

Responsibilities

• Design, build, test and maintain robust batch and near-real-time data pipelines and ELT workflows.
• Lead technical design, evaluate trade-offs and ensure scalable, secure solutions.
• Own solutions through the full engineering lifecycle from design to monitoring.
• Mentor engineers and contribute to shared patterns and standards.
• Partner with stakeholders to clarify requirements, dependencies and data ownership.
• Enable trusted data for analytics and AI use cases while ensuring privacy and access controls.

Skills

SQL

Python

PySpark

Databricks

Cloud data platforms

Data modelling

Education

Bachelor’s degree in a relevant discipline

Tools

dbt

Ab Initio

Snowflake

AWS

Git

Job description

• Design, build, test and maintain robust batch and near-real-time data pipelines and ETL/ELT workflows across enterprise systems, APIs, SaaS platforms and cloud data environments
• Develop reusable, well-documented data models and domain-aligned data products, translating complex business logic into reliable engineering solutions
• Lead technical design for assigned initiatives, evaluate trade-offs, and ensure solutions are scalable, secure, maintainable and cost-effective.
• Own solutions throughout the engineering lifecycle, from requirements and design through deployment, monitoring, incident resolution and continuous improvement
• Implement automated testing, data validation, reconciliation, lineage, observability, alerting and operational runbooks
• Apply engineering practices including version control, code review, CI/CD, infrastructure automation and appropriate change controls; improve performance and platform efficiency.
• Mentor engineers, lead technical reviews and contribute to shared patterns, coding standards and communities of practice
• Partner with business stakeholders, architects, security, governance, analytics and platform teams to clarify requirements, dependencies and data ownership
• Proactively identify delivery risks, technical debt and cross-team dependencies; communicate progress, options and recommendations clearly
• Enable trusted data for analytics, planning, automation and AI use cases while respecting privacy, security and access controls

Strong hands-on SQL and Python skills, with experience in distributed data processing using PySpark and/or DatabricksAbility to influence technical decisions, mentor peers and communicate effectively with engineering and non-technical stakeholdersSolid experience with cloud data platforms and warehouses, particularly Snowflake; knowledge of data modelling, dimensional design and data product principlesUnderstanding of data security, access management, data quality, governance and regulatory considerations in enterprise environmentsBachelor’s degree in a relevant discipline or equivalent practical experienceTypically 6+ years of professional data engineering or software engineering experience, including ownership of complex production data solutionsExperience designing data pipelines and transformations using modern ETL/ELT tools and frameworks, such as dbt, Ab Initio or equivalent technologiesExperience with orchestration, Git-based workflows, automated testing, CI/CD, monitoring and troubleshooting production data servicesEnterprise integration experience with SAP S/4HANA, financial planning systems, supply chain applications, ServiceNow or HR platformsExperience building reconciled financial data, master/reference data or enterprise operational data productsExperience with AWS services, APIs, event-driven integration and infrastructure as codeExposure to AI/ML data preparation, data observability, metadata management and modern data governance practicesExperience working with globally distributed teams and supporting cross-functional transformation programmes