Senior Data Engineer
Posted
Description
- We are looking for an experienced Senior Data Engineer to design, develop and operate trusted, scalable data solutions that power decision-making and transformation across Sony Interactive Entertainment (SIE). Working within Player Data Engineering, you will build reusable data pipelines, integration services and curated data products for business domains including Commerce, hardware, game play, products and services, and accounts
- This is a hands-on senior individual contributor role. You will partner with engineering, platform, product and business teams. Take end-to-end ownership of complex solutions and raise engineering standards through technical leadership and mentoring. You will help create reliable, governed and AI-ready Player data foundations
- 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 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 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
- Experience with AWS services, APIs, event-driven integration and infrastructure as code
- Familiarity with building or integrating AI agents into data engineering workflows would be a strong plus
- Experience leveraging AI coding assistants, agentic workflows, and AI-driven development tools to accelerate engineering productivity and improve code quality
- Typically 5+ years of professional data engineering or software engineering experience, including ownership of complex production data solutions
- Solid experience with cloud data platforms and warehouses, particularly Snowflake; knowledge of data modelling, dimensional design and data product principles
- Experience designing data pipelines and transformations using modern ETL/ELT tools and frameworks, such as dbt, Ab Initio or equivalent technologies
- 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
- 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
- Bachelor’s degree in a relevant discipline or equivalent practical experience
- Experience working with globally distributed teams and supporting cross-functional transformation programs
- Exposure to AI/ML data preparation, data observability, metadata management and modern data governance practices
Skills
- Agentic AI
- 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
- Kubernetes
- Machine Learning
- Metadata Management
- Observability
- PySpark
- Python
- Snowflake
- SQL
- Test Automation
- Version Control