Senior Data Engineer

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 Enterprise Data Engineering, you will build reusable data pipelines, integration services and curated data products for business domains including Finance, Supply Chain, IT Operations, People & Places, and Sales & Operations.

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 enterprise data foundations.

Responsibilities

Design and deliver enterprise data solutions

·       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 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 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.

Skills and Qualifications

·       Typically 6+ years of professional data engineering or software engineering experience, including ownership of complex production data solutions.

·       Strong hands-on SQL and Python skills, with experience in distributed data processing using PySpark and/or Databricks.

·       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, 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.

·       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.

Desirable Experience

·       Enterprise integration experience with SAP S/4HANA, financial planning systems, supply chain applications, ServiceNow or HR platforms.

·       Experience with AWS services, APIs, event-driven integration and infrastructure as code.

·       Experience building reconciled financial data, master/reference data or enterprise operational data products.

·       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.