Senior Manager, Solution & Data Architecture
PANDORA Denmark
Senior Manager, Solution & Data Architecture We’re looking for a Senior Manager, Data & AI Architecture, based in Copenhagen or London, to lead our team of four Data, AI and Solution Architects and serve as the design authority for Pandora’s Data & AI platform. This is a genuine dual role. You’ll set the architectural direction and standards, while remaining close enough to the technology to challenge, review and approve the designs that matter. Our target architecture is Databricks, including Delta Lake, medallion patterns and Unity Catalog, while Azure Synapse and its conformed enterprise models continue to power a significant part of the estate across supply chain, commerce, manufacturing, corporate technology and omni-channel. A critical part of the role is leading the transition to Databricks: shaping the target architecture, determining where ownership sits as the platform evolves, and ensuring existing solutions remain coherent and supported throughout the journey. The role is approximately 60% architecture and 40% people leadership. You’ll lead and develop four architects, but this is not a role where technical accountability is delegated to the team. Both sides of the role are real, and you’ll be expected to lead both. Scope and authority
- Design authority for Data & Analytics: approves data and AI solution designs, and can require rework, attach conditions to an approval, or withhold approval.
- Chairs the architecture review forum, setting its standards, cadence and quality gates, and deciding what requires review against what is already covered by an approved pattern.
- Resolves conflicting design positions across architects, lead engineers and delivery teams.
- Owns reference architectures, solution patterns, data contracts and architecture decision records — accountable for their adoption, not just their publication.
- Owns the team's roadmap, capacity allocation across workstreams, and budget, including engagement of external consulting resources.
- Translates and enforces the Data & AI architecture strategy set by the Global Director, and informs how it evolves.
- Set and enforce the standards for what is built on Databricks as the target architecture, what remains on Synapse for now, and what is retired — and on what evidence.
- Decide how data products are aligned: source-aligned versus domain-aligned versus consumer-aligned, where ownership and stewardship sit, and where a conformed enterprise model (CDM/RDM) serves better than distributed ownership.
- Review technical designs from solution architects and lead engineers against platform standards; give feedback that changes the design, not just documents it.
- Define and evolve reference architectures, solution patterns and data contracts so that quality is built in rather than inspected in.
- Lead design sessions with architects, engineering and business stakeholders on complex or contested problems.
- Oversee transitional architectures through to legacy decommission, maintaining business continuity and closing the parallel run.
- Set and enforce architecture standards for agentic systems: orchestration, tool and data access boundaries, evaluation, guardrails and observability.
- Set and enforce architecture standards for ML systems: feature reuse, training and serving topology, model lifecycle, and monitoring for drift.
- Approve AI solution designs with enough depth to arbitrate them on the technical merits, and in line with AI policy and applicable regulation.
- Embed privacy-by-design and personal data handling requirements into platform patterns, particularly across commerce and omni-channel.
- Maintain computational governance: data quality frameworks, contracts and lineage as platform capability rather than project effort.
- Line management for four architects: coaching, performance, career development, hiring.
- Establish ways of working — review process, documentation standards, quality gates — and hold the team to them.
- Represent architecture in leadership forums, steering committees and cross-functional planning.
- Keep architects hands-on and embedded with engineering teams; architecture produced at a distance from delivery does not land.
- A published and adopted standard for what is built on Databricks, with evidence of designs changed by it.
- A clear model for data product alignment and ownership, applied on live work rather than documented in isolation.
- An architecture review forum running to a predictable cadence, with a visible ADR record and a known path for challenging a decision.
- Approved agentic and ML reference patterns in use.
- At least one legacy component fully decommissioned, not just superseded.
- A development plan in place for each of the four architects, and any open roles filled.
- 5+ years designing and delivering production data platform solutions at enterprise scale, hands-on.
- At least 2 years line-managing technical specialists, including performance management, career development and hiring.
- Has held a design approval or architecture governance mandate, including declining designs and making that decision hold.
- Has led a platform migration or a parallel-run estate through to legacy decommission.
- Deep working knowledge of Databricks (Delta Lake, medallion, Unity Catalog) as a target architecture, plus enough Azure Synapse (star schemas, conformed models, dedicated pools) to govern what remains there, and pipeline orchestration with ADF or Databricks Workflows.
- Has designed source-aligned, domain-aligned and consumer-aligned data products, with a clear view of where distributed domain ownership beats a conformed enterprise model (CDM/RDM) and where it does not.
- Has implemented or governed a domain ownership model — data mesh or similar — including the federated governance and stewardship that makes it work.
- Has architected agentic and ML systems: orchestration, tool and data access boundaries, evaluation and guardrails; feature and model lifecycle, serving, and monitoring for drift.
- Has set architecture standards that others adopted — solution patterns, reference architectures and architecture decision records.
- Has implemented data quality frameworks, data contracts or computational governance in production.
- Holds an architectural position under delivery pressure, and judges well when to concede.
- Communicates architecture clearly to technical and business audiences, including solution designs, ADRs and data flow diagrams.
- Understanding of retail, manufacturing, finance or supply chain business processes and how data flows through a business.
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