Senior Data Engineer

Division: Shared Services Marcura's data team is an AI-first data engineering organisation with significant ownership and opportunity to drive impact across the organisation and for our customers. We work across finance, commerial, and engineering to make sure everyone has access to accurate, timely and complete data within the constraints of their role. The data team own the data pipelines, modelling and warehousing required to support our stakeholders in the tools they use, be it AI models, dashboards, spreadsheets, or CRM tools. We are looking for a data engineer to own and devloping the data pipelines and core data models for Marcura. If you're passionate about working as an AI-first data engineer and are eager to create solutions with significant impact, we'd love to hear from you. Job Responsibilities 1. Model Development: Build and maintain dbt models in a complex multi-product data environment. 2. Source System Integration: Integrate new source systems into the warehouse using Fivetran or Apache Airflow. Define tests, manage source freshness, and coordinate with upstream engineering teams on schema changes and breakages. 3. Data quality, testing, and reliability: Write dbt tests on the right grains. Set up monitoring and alerting on critical models so issues are caught before stakeholders notice. Own incident response for owned models. 4. BigQuery Performance and Cost Optimisation: Keep warehouse cost and query performance under control. Use partitioning, clustering, and incremental materialisations where appropriate. Investigate and refactor slow or expensive queries. Make conscious build-versus-rebuild trade-offs for incremental models. 5. PII, RBAC, and Compliance: Implement PII hashing. Support the role-based access control work for both internal users and external customer-facing views. Ensure new models comply with the data governance and compliance standards expected at Marcura. 6. End-user access: Make sure modelled data lands in BI tools, CRMs and MCPs servers in a usable shape. Partner with the tool owners on metric definitions, dimension/measure design, and dashboard reliability. Syncing model changes downstream is part of the job. 7. Stakeholder Partnership: Work directly with Commercial, Customer Success, Finance, Compliance, and Product teams to understand what they need from the data platform. Translate fuzzy business questions into concrete datasets and metrics. Push back when a request is the wrong shape; commit fully when it is the right shape. 8. AI-Augmented Engineering: Use AI tooling (Claude Code or Codex and Github Actions) as a daily part of the engineering workflow — for code generation, code review, model documentation, and debugging. Help raise the AI fluency of the wider Data team and contribute reusable agent skills. 9. Data dictionary: Document every model, source, and macro you own. Keep the dbt dictionary clean and searchable so other engineers and analysts can self-serve. Write commit messages, PR descriptions, and incident post-mortems that explain the why, not just the what. 10. Operational Reliability and On-Call: Share responsibility for the platform being up and trusted. Respond to data incidents, take part in the on-call rotation as it evolves, and contribute to runbooks and post-mortems. Requirements: - Bachelor degree in Computer Science, Engineering, Mathematics, Data Science, or related discipline. - 3 to 5 years experience in a hands-on data engineering or analytics engineering role, ideally in a B2B SaaS or B2B data product environment. - Demonstrated experience shipping production dbt models on a cloud data warehouse (BigQuery, Snowflake, Redshift, or similar). - Track record of owning data pipelines end-to-end — from source ingestion through transformations to BI/consumption layer — and partnering directly with non-technical stakeholders to deliver useful datasets. - You have spent the past six months using LLMs to write code and know how to direct an AI agent to get work done efficiently, and what work requires your judgement Skills and Knowledge - Advanced SQL and dimensional modelling skills (Kimball-style star schemas). - Strong hands-on dbt experience: tests, documentation, macros, sources, snapshots, and CI workflows. - Working knowledge of BigQuery (or another cloud data warehouse) including performance tuning, partitioning, clustering, and cost awareness. - Comfortable with tools like Fivetran and Apache Airflow for orchestration, ingestion scripts, and tooling. - Git, code review, and CI/CD discipline. - Understanding of PII handling, data privacy, and role-based access control as applied to analytics data. - Familiarity with at least one BI tool in the Marcura stack (PowerBI, Metabase, Lightdash) and how data models surface to end users. - Strong written communication; able to write documentation, dbt model descriptions, and change rationales that the next engineer ca

Model Development: Build and maintain dbt models in a complex multi-product data environment. Source System Integration: Integrate new source systems into the warehouse using Fivetran or Apache Airflow. Define tests, manage source freshness, and coordinate with upstream engineering teams on schema changes and breakages. Data quality, testing, and reliability: Write dbt tests on the right grains. Set up monitoring and alerting on critical models so issues are caught before stakeholders notice. Own incident response for owned models. BigQuery Performance and Cost Optimisation: Keep warehouse cost and query performance under control. Use partitioning, clustering, and incremental materialisations where appropriate. Investigate and refactor slow or expensive queries. Make conscious build-versus-rebuild trade-offs for incremental models. PII, RBAC, and Compliance: Implement PII hashing. Support the role-based access control work for both internal users and external customer-facing views. Ensure new models comply with the data governance and compliance standards expected at Marcura. End-user access: Make sure modelled data lands in BI tools, CRMs and MCPs servers in a usable shape. Partner with the tool owners on metric definitions, dimension/measure design, and dashboard reliability. Syncing model changes downstream is part of the job. Stakeholder Partnership: Work directly with Commercial, Customer Success, Finance, Compliance, and Product teams to understand what they need from the data platform. Translate fuzzy business questions into concrete datasets and metrics. Push back when a request is the wrong shape; commit fully when it is the right shape. AI-Augmented Engineering: Use AI tooling (Claude Code or Codex and Github Actions) as a daily part of the engineering workflow — for code generation, code review, model documentation, and debugging. Help raise the AI fluency of the wider Data team and contribute reusable agent skills. Data dictionary: Document every model, source, and macro you own. Keep the dbt dictionary clean and searchable so other engineers and analysts can self-serve. Write commit messages, PR descriptions, and incident post-mortems that explain the why, not just the what. Operational Reliability and On-Call: Share responsibility for the platform being up and trusted. Respond to data incidents, take part in the on-call rotation as it evolves, and contribute to runbooks and post-mortems.