Data Architect
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Job Description
Data Architect managing the data architecture across OneOcean's data organization. Collaborating with engineering, BI, and AI teams for maritime solutions.
Responsibilities:
- Own the data architecture across OneOcean's data organisation — operational, lakehouse, analytical-serving and vector layers — providing a coherent target state and a pragmatic path to it.
- Lead the discovery, documentation and specification of the data structures and models required to support product and analytics roadmaps.
- Reverse-engineer source data from OneOcean SaaS products — building accurate logical models of upstream systems and identifying the data contracts, grain and semantics our pipelines depend on.
- Design the right data store for each use case — choosing between OLTP, columnar / OLAP, lakehouse and vector approaches; making the trade-offs explicit.
- Define and maintain conceptual, logical and physical data models; produce clear ERDs, lineage and dimensional designs (star / snowflake, conformed dimensions, surrogate keys).
- Establish and enforce modelling standards, naming conventions, data contracts and schema-evolution practices across teams.
- Partner with the Data Team Lead, BI Team Lead and AI Team Lead — translating product and analytics needs into specifications their engineers can build from, and unblocking architectural decisions as they arise.
- Collaborate with Product, Architecture and Engineering Team Leads to align data direction with the wider engineering strategy.
- Champion data governance — cataloguing, lineage, ownership, quality, security and privacy.
- Document architectural decisions (ADRs) so the why is preserved alongside the what.
- Mentor engineers across the data organisation on modelling, design and architectural reasoning — without line-managing them.
- Stay current with the data landscape and bring in proven techniques as they mature; foster a culture of continuous improvement, innovation and knowledge sharing.
Requirements:
- 8+ years of commercial experience in data engineering, BI engineering or data architecture roles, with significant time spent on modelling and specification.
- Demonstrable experience owning data architecture across multiple workloads — operational, analytical, lakehouse and (ideally) AI / vector.
- Deep data-modelling expertise — conceptual, logical and physical; dimensional / star-schema; SCD; data contracts; semantic-layer design.
- Strong SQL across multiple dialects; comfortable reading and reasoning about complex source-system schemas.
- Proven ability to reverse-engineer SaaS / operational systems — discovering grain, primary keys, relationships, soft-delete patterns and quirks that aren't in the docs.
- Solid working knowledge of analytical serving (e.g. Apache Druid, Apache Superset, Power BI) and the modelling patterns that make them perform.
- Working knowledge of lakehouse approaches (e.g. Delta Lake) — MERGE semantics, partitioning, schema evolution.
- Pragmatic decision-making — able to balance ideal architecture against delivery pressure and explain the trade-offs clearly.
- Excellent written and visual communication — produces specifications and diagrams that engineers can build from with minimal ambiguity.
- Exceptional collaboration and stakeholder skills — comfortable bridging engineering, BI, AI, product and business audiences.
- Documentation discipline — Confluence-grade architectural records, ADRs, model dictionaries and onboarding material.
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