Core details
Company type: International Retail Group – In-house Data & Engineering
Location: Cluj-Napoca, Romania
Work model: Hybrid
Engagement: Employment Contract, Permanent, Full-Time
Role level: Principal level – Senior Individual Contributor
Experience level: 8+ Years
Key Skills / Tech Stack: Python, SQL, Spark / PySpark / Scala, dbt, Databricks, BigQuery, Azure Data Factory, GCP / Azure / AWS, CI/CD, Terraform, Docker / Kubernetes, Great Expectations, data architecture (batch, streaming, event-driven)
Department: Engineering / Data
Industry: Retail / Tech and digital
About the client
Our client is a large international retail group with an in-house engineering organisation – technology is built internally, not outsourced. Teams own what they design and run, end to end, on platforms that serve the group’s markets and brands across Europe.
The Cluj engineering hub is part of a wider international ecosystem. The data function is going through a period of transformation, and the local capability continues to grow.
About the role
As Principal Data Engineer, you are the go-to technical authority for data engineering across the organisation. This is a senior individual contributor role, not a people management one. Your influence comes from expertise, direction and the standards you set, across multiple squads and domains.
You will define the data platform and data engineering strategy, own architecture decisions and patterns, and shape long-term roadmaps together with Architecture and Product. You will work with senior stakeholders across Engineering, Product, Architecture and the Tech Board, and lead complex initiatives that span systems, teams and geographies.
You will also be a central figure in the Data Engineering Chapter. You will mentor engineers, contribute to hiring and capability planning, and build a culture of learning, experimentation and technical excellence.
Responsibilities:
Define and lead the data platform and data engineering strategy, aligned with wider technology objectives.
Own architecture decisions, patterns and standards across data pipelines, storage and processing.
Establish and evolve data engineering principles, frameworks and best practices.
Partner with Architecture and Product to shape long-term roadmaps and platform capabilities.
Ensure solutions are scalable, resilient, performant and cost-efficient.
Drive the design and implementation of production-grade data pipelines and platforms.
Champion modern engineering practices (CI/CD, testing, observability, DevOps) and standards for data quality, governance and security.
Promote reuse through data products, shared services and platform capabilities.
Act as technical leader across multiple squads, providing clear direction and decision-making guidance.
Lead large-scale, business-critical data initiatives, balancing speed, quality, cost and risk.
Mentor and coach engineers across the Data Engineering Chapter, and contribute to hiring and onboarding.
Stay ahead of industry trends, bring in new tools and approaches where they add value, and challenge existing ways of working.
Qualifications:
Strong experience with distributed data processing systems and architectures.
Proven track record delivering large-scale data platforms and pipelines.
Expertise in data modelling, storage, access and processing patterns.
Deep understanding of data architecture patterns (batch, streaming, event-driven) and cloud-native, distributed systems design.
Experience designing for resilience, scalability, observability, performance and cost efficiency.
Strong Python and SQL, with Spark / PySpark (Scala is a plus).
Hands-on experience with dbt and modern data platforms such as Databricks, BigQuery or Azure Data Factory.
Experience with at least one major cloud platform (GCP, Azure or AWS).
DevOps / DataOps experience: CI/CD, Git, Terraform, Docker / Kubernetes.
Familiarity with data quality and testing frameworks such as Great Expectations.
Ability to influence at executive…