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# Data Engineer

- **Company:** Sony Interactive Entertainment Europe Limited
- **Location:** London, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Data Analysis, Business Logic, Automation of Tests, Cloud Database, Software Quality, Code Review, Continuous Integration, Data Validation, Data Cleansing, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Security, Data Systems, Programming Tools, Distributed Computing Environment, Identity and Access Management, Python (Programming Language), Meta-Data Management, Operational Databases, Cloud Services, DataOps, Software Engineering, SQL Databases, Snowflake, Technical Debt, Ab Initio, AI Coding Agents, Agentic-AI, Pyspark, Git Flow, Kubernetes, Infrastructure Automation Frameworks, Software Coding, Software Version Control, Data Pipelines, Databricks
- **Published:** October 24, 2026
- **Apply:** https://job-boards.greenhouse.io/sonyinteractiveentertainmentglobal/jobs/6232151004

## About the Role

* Typically 5+ years of professional data engineering or software engineering experience, including ownership of complex production data solutions.
 * Experience designing data pipelines and transformations using modern ETL/ELT tools and frameworks, such as dbt, Ab Initio or equivalent technologies.
 * Solid experience with cloud data platforms and warehouses, particularly Snowflake; knowledge of data modelling, dimensional design and data product principles.
 * Experience with orchestration, Kubernetes, Git-based workflows, automated testing, CI/CD, monitoring and troubleshooting production data services.
 * Understanding of data security, access management, data quality, governance and regulatory considerations in enterprise environments.
 * Experience leveraging AI coding assistants, agentic workflows, and AI-driven development tools to accelerate engineering productivity and improve code quality.
 * Familiarity with building or integrating AI agents into data engineering workflows would be a strong plus.
 * Ability to influence technical decisions, mentor peers and communicate effectively with engineering and non-technical stakeholders.
 * Bachelor's degree in a relevant discipline or equivalent practical experience., * Strong hands-on SQL and Python skills, with experience in distributed data processing using PySpark and/or Databricks.
 * Experience with AWS services, APIs, event-driven integration and infrastructure as code.
 * Exposure to AI/ML data preparation, data observability, metadata management and modern data governance practices.
 * Experience working with globally distributed teams and supporting cross-functional transformation programs.

## Description

* Design, build, test and maintain robust batch and near-real-time data pipelines and ETL/ELT workflows across Player data systems, APIs, Snowflake Db 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 quality, reliability and operations

 * 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.

Provide technical leadership and collaborate

 * Mentor engineers, lead technical reviews and contribute to shared patterns, coding standards and communities of practice.
 * Partner with TPM's, 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.