Data Architect

Ocean Technologies Group

Data Architect managing the data architecture across OneOcean's data organization. Collaborating with engineering, BI, and AI teams for maritime solutions.

Posted 7/15/2026full-timeRemote • 🇷🇴 RomaniaSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates extensive expertise in data architecture, including conceptual, logical, and physical data modeling, as well as proficiency in SQL and analytical serving tools. Capable of leading data governance initiatives and collaborating effectively across teams to align data strategies with business objectives.

Highest-signal resume keywords

Data Architecture OwnershipDeep Data Modeling ExpertiseStrong SQL ProficiencyAnalytical Serving KnowledgeLakehouse Approach Familiarity

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ModelingSQLData ArchitectureDimensional DesignReverse EngineeringData ContractsSchema EvolutionData GovernanceDocumentation DisciplinePragmatic Decision-Making

Soft Skills

CollaborationCommunicationMentoringStakeholder ManagementContinuous Improvement

Tools & Technologies

Apache DruidApache SupersetPower BIDelta LakeConfluence

Industry Keywords

Operational WorkloadsAnalytical WorkloadsLakehouseAIVector Approaches

Tech Stack

Tools & technologies

ApacheSQL

About the role

Key responsibilities & impact

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

What you’ll need

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.

Benefits

Comp & perks

🌐 Worldwide ❌ Jobs You've Hidden ⭐️ Saved Jobs ✅ Applied Jobs ✉️ Email Alerts 👤 Account Ocean Technologies Group Website LinkedIn All Job Openings 201 - 500 employees Founded 2020 🛍️ eCommerce 📚 Education 🚗 Transport 💰 Venture Round on 2015-05 eCommerce
Education
Transport Ocean Technologies Group is a company dedicated to transforming the maritime industry through innovative e-learning and crew management solutions. They provide a comprehensive library of maritime training courses, competency management systems, and performance evaluation tools to empower maritime professionals and support organizations in meeting compliance and operational excellence. By leveraging technology, they aim to enhance the skills of seafarers and optimize vessel performance in a rapidly evolving industry. Data Architect Job not on LinkedIn 🔥 7 minutes ago 🇷🇴 Romania – Remote ⏰ Full Time 🟠 Senior 🔴 Lead 🚰 Data Engineer Apache SQL Apply Now Find Hiring Managers Customize resume + cover letter Report problem ☆ Save ☑️ Mark as applied ❌ Hide 📋 Description
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.
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