Senior Data Architect

The Data Technical Architect is the design authority for the data architecture that underpins the Command Centre and enables the Control Tower. The role owns how operational data from source systems across product domains is ingested, integrated, resolved, modelled, certified, and served as a single, trusted, near real‑time view of the business.

This is a data‑architecture‑led role. The primary deliverable is the target‑state data architecture – layering, canonical models, event and entity design, the semantic and KPI layer, and the data quality and lineage framework – that allows the Control Tower to detect exceptions, trigger alerts, and support root‑cause analysis with confidence in the underlying data.

The role works closely with Command Centre operations, BI delivery, Data Engineering, GDA, Data Platform, source system owners, and Enterprise Architecture, and operates within the standards set by the Tech Governance Lead.

The Data Technical Architect is the design authority for the data architecture that underpins the Command Centre and enables the Control Tower. The role owns how operational data from source systems across product domains is ingested, integrated, resolved, modelled, certified, and served as a single, trusted, near real‑time view of the business.

This is a data‑architecture‑led role. The primary deliverable is the target‑state data architecture – layering, canonical models, event and entity design, the semantic and KPI layer, and the data quality and lineage framework – that allows the Control Tower to detect exceptions, trigger alerts, and support root‑cause analysis with confidence in the underlying data.

The role works closely with Command Centre operations, BI delivery, Data Engineering, GDA, Data Platform, source system owners, and Enterprise Architecture, and operates within the standards set by the Tech Governance Lead.

Key Responsibilities

Data Architecture & Design

Own the end‑to‑end data architecture for the Command Centre – how data flows from operational source systems across product domains (e.g. Contracts, Ocean) through to the Control Tower consumption layer.

Define the target‑state data architecture and transition roadmap, including the layering model (landing, raw, integrated/curated, consumption) and the storage, compute, and serving choices at each layer.

Design the canonical, cross‑domain data layer that gives the Control Tower one consistent representation of the core business objects, independent of the source systems they originate from.

Define master and reference data requirements, entity resolution, and cross‑system key mapping so that events from different systems resolve to the same business entity.

Establish data domain boundaries, data ownership, and data product definitions aligned to the Command Centre operating model.

Produce and maintain core architecture artefacts – conceptual, logical, and physical data models, data flow and lineage diagrams, integration maps, and architecture decision records.

Data Integration & Pipeline Architecture

Architect real‑time, near real‑time, and batch ingestion patterns – streaming, event‑driven, CDC, API, and scheduled – and define which pattern applies to which source and use case.

Define the operational event model and payload standards for the milestones, status changes, and exceptions that drive Control Tower views.

Design pipeline patterns for reliability at scale: idempotency, replay, late‑arriving and out‑of‑order events, backfill, and reconciliation back to source.

Set standards for orchestration, dependency management, error and dead‑letter handling, and pipeline observability.

Define and agree data contracts and SLAs with source system teams, covering schema, frequency, volume, and change management.

Define latency, freshness, and throughput targets per data domain and ensure the architecture demonstrably meets them.

Data Modelling & Semantic / KPI Layer

Design the dimensional, event, and state models required for end‑to‑end milestone tracking, cycle time, dwell, and exception analysis.

Architect the semantic and KPI layer so that each metric is defined once, certified, and reused consistently across Control Tower views and downstream reporting.

Model for drill‑down, so an alert on the Control Tower can be traced to transaction‑level detail and root cause without leaving the platform.

Define the historisation approach – slowly changing dimensions, snapshots, and point‑in‑time reporting – to support trend and retrospective analysis.

Design aggregation, pre‑computation, and caching strategies so Control Tower dashboards meet response‑time expectations at production data volumes.

Data Quality, Governance & Security

Define data quality dimensions, rules, and thresholds at each architectural layer, and design how failures are detected, quarantined, and escalated.

Ensure Control Tower alerting is built on trusted data – design the freshness, completeness, and validity checks that gate what is surfaced to operations.

Design end‑to‑end metadata and lineage capture, from source system field through to published KPI.

Design role‑based access, row and column level security, masking, retention, and audit into the data architecture rather than bolting it on later.

Ensure alignment with enterprise data architecture, cloud strategy, and BI governance standards, and work with the Tech Governance Lead on dataset certification.

Identify, document, and drive remediation of data architecture technical debt.

Delivery & Execution

Translate the architecture into sprint‑ready designs, data contracts, and backlog items for data engineering and BI delivery teams.

Provide hands‑on technical direction – prototypes, proofs of concept, performance tuning, and resolution of complex modelling and pipeline problems.

Review data models, pipelines, and semantic layers for adherence to the agreed architecture.

Own volumetrics and capacity planning, and support performance, resilience, and reconciliation testing ahead of each Control Tower release.

Drive reuse of data products, models, and patterns across Command Centre use cases to reduce duplication and rework.

Stakeholder & Cross-functional Collaboration

Partner with Command Centre operations and business owners to translate operational visibility and exception‑management needs into concrete data requirements.

Collaborate closely with Data Engineering and Platform teams, GDA, source system owners, Enterprise Architecture, Security, and Infrastructure and Cloud teams.

Present data architecture proposals to design authorities and governance forums for review and approval.

Communicate data design trade‑offs, risks, and dependencies clearly to non‑technical stakeholders.

Mentor data engineers and modellers on architecture patterns, modelling standards, and design quality.

Required Qualifications / Skills

Education & Experience

Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Engineering, or a related field.

Extensive experience as a data architect designing large‑scale, multi‑source data platforms, with hands‑on delivery experience rather than documentation only.

Proven experience designing the data architecture behind a control tower, command centre, or comparable real‑time operational visibility solution.

Track record of integrating operational and transactional source systems across multiple business domains.

Certifications in cloud data platforms, data architecture, or frameworks such as DAMA‑DMBOK or TOGAF are a plus.

Technical Competencies

• Deep data modelling expertise – conceptual, logical, and physical modelling; dimensional (Kimball), Data Vault, and event/state modelling.
• Strong experience with cloud data platforms, lakehouse and warehouse architectures, and layered (medallion‑style) design.
• Hands‑on experience with streaming and event‑driven architectures, CDC, message/event platforms, and API‑based integration.
• Advanced SQL and data performance engineering – partitioning, clustering, incremental processing, query tuning, and cost optimisation.
• Experience with orchestration and transformation frameworks and with data quality and observability tooling.
• Working knowledge of master data management, reference data, and entity resolution techniques.
• Experience with metadata, lineage, and data catalogue tooling, and with certified dataset practices.
• Strong understanding of data security, privacy, compliance, and governance, and the ability to design to them.
• Sufficient BI and visualisation knowledge to design a semantic layer that Control Tower dashboards can consume efficiently.
  
  

Leadership & Behavioural Competencies

Strong stakeholder management and influencing skills, particularly with source system owners.

Ability to communicate complex data concepts clearly and effectively.

High attention to detail combined with strategic, big‑picture thinking.

Sound judgement under operational pressure and in incident‑driven environments.

Proven ability to work across teams and drive alignment without direct authority.

Maersk is committed to a diverse and inclusive workplace, and we embrace different styles of thinking. Maersk is an equal opportunities employer and welcomes applicants without regard to race, colour, gender, sex, age, religion, creed, national origin, ancestry, citizenship, marital status, sexual orientation, physical or mental disability, medical condition, pregnancy or parental leave, veteran status, gender identity, genetic information, or any other characteristic protected by applicable law. We will consider qualified applicants with criminal histories in a manner consistent with all legal requirements.

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