Senior Data Analyst

What We’re Looking For

The purpose of this role is to turn large, complex maritime datasets into clear, defensible analysis that informs advisory engagements and strengthens our data products. AIS and other spatio-temporal data sit at the centre of the work, and most projects begin with an ambiguous question and we are therefore looking for someone who can frame the problem, build the pipeline, run the analysis, and state plainly what the results do and do not support.

The role spans the full analytics lifecycle. You will not hand off between an "analyst" and an "engineer"; you will access and model the data, interrogate it, and produce the output, whether that is a quick extract, a map, a dashboard, or a written deliverable a client will act on. Because we are moving towards automation and self-service, the role also carries a build dimension: codifying repeatable analyses into reusable components rather than rebuilding them each time.

What Success Looks Like

In the first few months, success means becoming productive in Databricks, contributing to live project work, and producing outputs that hold up to technical and client scrutiny. Within the first year, you are trusted to take a loosely defined question and return an answer that is reproducible, well-validated, and clearly caveated, with minimal rework.

Sustained success is measured on two axes. The first is delivery: clients act on your analysis, and your work is reliable, defensible, and reproducible. The second is leverage: the repeatable parts of your work become tooling the team reuses, so each engagement starts further forward than the last. Further, success additionally means raising the technical standard around you, through peer review, mentoring, and ownership of specific data products or workstreams.

Essential

What You Bring

• Strong analytical ability, with a track record of turning ambiguous, open-ended questions into defensible answers, not running pre-defined reports.
• Proficiency in SQL and Python for analysis.
• Experience with large datasets, and the judgement to know when an approach will not scale.
• A working grasp of data quality, validation, and reproducibility as habit.
• Comfort with ambiguity and incomplete requirements, and the discipline to state what the data does and does not support.
• Git and collaborative development.
• Clear written and verbal communication in English with non-technical stakeholders.
• A quantitative or analytical degree, or equivalent demonstrable experience.
  
  

Preferred

• Production Databricks experience (Delta, medallion, job orchestration), or strong evidence of fast ramp on an equivalent distributed platform.
• Spatial analysis at scale: PySpark, Spark SQL, Spark spatial SQL, H3 indexing, or comparable distributed geospatial work.
• Familiarity with AIS or comparable spatio-temporal data (positions, port calls, voyages, activity states).
• Ingestion from heterogeneous sources: SQL/Postgres, web scraping, and third-party APIs.
• Exposure to serving layers: natural-language-to-SQL (Genie or similar) or lightweight data-product apps.