Senior Data Engineer

We're looking for an experienced Senior Data Engineer to join the Insights & Enablement team within Retail Media Tech s team sits at the heart of JET's retail media data platform, responsible for the infrastructure, pipelines, and data products that power how advertisers understand, optimise, and grow their campaigns on the JET platform.

This is not a typical Senior Data Engineer role. You'll work across the full stack of modern data engineering ranging from event streaming and attribution pipelines to Gold Layer data marts and AI-powered tooling and you'll help shape the technical foundation that underpins our retail media platform 'll partner closely with data scientists, analytical engineers, product managers, and ML engineers to turn complex data challenges into reliable, scalable production systems.

We're building a lot: a Top Rank Sponsored Listings data platform on Big Query and dbt, a semantic layer serving Tableau and Looker Studio, Snowplow-based event attribution infrastructure, and agentic AI tooling for Post-Campaign Analysis. You'll have the opportunity to work on all of it.

Critically, this role operates in a highly cross-functional environment. You'll work alongside JET's central Data & Analytics organisation, Platform Engineering, and other technology teams aligning on shared infrastructure standards, contributing to joint working groups, and ensuring retail media data products integrate cleanly with the broader JET data ecosystem. As a senior member of the team, you'll do more than code you'll help define best practices, mentor engineers, and raise the technical bar across the team.

• Design and build scalable data pipelines and event streaming architectures across batch and near-real-time workloads, with a focus on reliability and data quality.
• Develop and maintain Gold Layer data marts in Big Query using dbt Core, with a strong grasp of top-down/bottom-up data modelling methodologies (Kimball, OBT, or equivalent).
• Build and maintain Airflow-orchestrated ELT workflows across the retail media data platform, including attribution pipelines supporting campaign performance and incrementality measurement.
• Contribute to Retail Media event tracking infrastructure, schema governance, SLA/SLO monitoring, and data quality validation across the unified events pipeline.
• Support the development of a semantic layer (Cube.dev/Helix) bridging Big Query Gold tables to downstream BI tooling.
• Begin to contribute to architecture discussions beyond your immediate team, identifying improvement points in individual components and the system as a whole, with a clear path to shaping platform-level decisions at Staff level.
• Embed Data Ops best practices across the team: CI/CD, testing, observability, data drift monitoring, and incident response using tools like Grafana and Datadog.
• Collaborate with ML engineers and data scientists to product ionise models and agentic tooling built on internal AI platforms, with hands-on use of AI-assisted development tools such as Claude.ai, OpenAI, Git Hub Copilot, and Cursor as part of your everyday engineering workflow.
• Work cross-functionally with Data & Analytics, Platform Engineering, and other JET technology teams, contributing to shared standards, joint initiatives, and platform-level decisions that affect the broader data estate.
• Mentor engineers within the team and contribute to technical documentation, decision-making frameworks, and engineering standards.

Demonstrable senior-level experience building and delivering data engineering solutions, including distributed systems, event-driven architectures, and large-scale data processing (batch and streaming).

• Strong Python skills and solid SQL across Big Query or equivalent cloud data warehouses; familiarity with Scala or Java is a plus.
• Deep expertise in data pipeline design: batch vs. streaming, ETL/ELT, schema design, partitioning, and efficient storage patterns.
• Hands-on experience with dbt Core (or dbt Cloud) and Airflow for orchestration and transformation at scale.
• Solid grounding in data warehouse and data lake architecture, including dimensional modelling and layered architectures (Gold/Silver/Bronze or equivalent).
• Production experience with GCP (Big Query, Dataflow, GCS) and AWS (S3, Iceberg, Kinesis, DynamoDB), you're comfortable operating across both clouds.
• Familiarity with OLAP and OLTP storage systems and an eagerness to build expertise in stream processing technologies such as Apache…