Senior Data Engineer
Posted Updated
Summary
Builds and maintains batch and near-real-time data pipelines, ETL/ELT workflows, and domain data products for PlayStation's enterprise systems, owning solutions from design through production support. Core stack: SQL, Python, PySpark/Databricks, Snowflake, dbt, and AWS, with mentoring and cross-team stakeholder work.
- 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 Databricks
- Ability to influence technical decisions, mentor peers and communicate effectively with engineering and non-technical stakeholders
- Solid experience with cloud data platforms and warehouses, particularly Snowflake; knowledge of data modelling, dimensional design and data product principles
- Understanding of data security, access management, data quality, governance and regulatory considerations in enterprise environments
- Bachelor’s degree in a relevant discipline or equivalent practical experience
- Typically 6+ 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
- Experience with orchestration, Git-based workflows, automated testing, CI/CD, monitoring and troubleshooting production data services
- Enterprise integration experience with SAP S/4HANA, financial planning systems, supply chain applications, ServiceNow or HR platforms
- Experience building reconciled financial data, master/reference data or enterprise operational data products
- 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 programmes
Skills
- AI
- Analytics
- API
- Automation
- AWS
- CI/CD
- Cloud
- Data Engineering
- Data Governance
- Data Modeling
- Data Pipelines
- Data Quality
- Databricks
- dbt
- ELT
- ETL
- Event Driven Architecture
- Git
- Infrastructure as Code
- Machine Learning
- Metadata Management
- Observability
- PySpark
- Python
- SaaS
- SAP
- SAP S/4HANA
- ServiceNow
- Snowflake
- SQL
- Test Automation
- Version Control