Marcura
Maritime
SeniorDataEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Data Engineer at Marcura. Skills: Data Engineering, dbt, BigQuery, AI tooling. Build and maintain dbt models. Integrate new source systems”
Industry & Context.
Performance optimisation; Cost optimisation; Query performance; Troubleshooting
On-call rotation
What They're Looking For.
Must Have
Bachelor degree in Computer Science, Engineering, Mathematics, Data Science, or related discipline, 3 to 5 years experience in data engineering or analytics engineering, Demonstrated experience shipping production dbt models on a cloud data warehouse, Track record of owning data pipelines end-to-end, Partnering directly with non-technical stakeholders, Spent past six months using LLMs to write code, Know how to direct an AI agent
Nice to Have
PhD preferred, Specific ML framework experience, Cloud platform certs
What You'll Do.
Build and maintain dbt models
Integrate new source systems
Define tests for source systems
Manage source freshness
Coordinate with upstream engineering teams
Set up monitoring and alerting
Own incident response for models
Keep warehouse cost under control
Keep query performance under control
and incremental materialisations
Investigate and refactor slow queries
Make build-versus-rebuild trade-offs
Implement PII hashing
Support role-based access control
Ensure new models comply with standards
Land modelled data in BI tools
Partner with tool owners on metric definitions
Partner with tool owners on design
Partner with tool owners on dashboard reliability
Sync model changes downstream
Work directly with teams to understand needs
Translate business questions into datasets
Push back on incorrect requests
Commit fully to correct requests
Use AI tooling for code generation
Use AI tooling for code review
Use AI tooling for model documentation
Use AI tooling for debugging
Raise AI fluency of Data team
Contribute reusable agent skills
Keep dbt dictionary clean
Write commit messages
Write PR descriptions
Write incident post-mortems
Share responsibility for platform reliability
Respond to data incidents
Take part in on-call rotation
Contribute to runbooks
Contribute to post-mortems
How You'll Work.
Team & Collaboration
Upstream engineering teams; Tool owners; Commercial teams; Customer Success teams; Finance teams; Compliance teams; Product teams; Wider Data team
Communication Scope
Documentation; Change rationales
Process & Methodology
CI/CD discipline
Full Job Description
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
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