Principal Data Consultant, Scale Factory

• 10+ years in data engineering and architecture, with at least 3 as the lead architect on complex, multi-team programmes
• Designed and delivered modern data platforms end to end on AWS and/or Databricks
• Still writes production-grade code: strong Python and SQL, Spark, dbt or equivalent, infrastructure-as-code (Terraform preferred)
• Track record in a consultancy, systems integrator or other client-facing setting — comfortable being accountable to a paying client, not only an internal stakeholder
• Data modelling depth across dimensional, Data Vault and lakehouse/medallion approaches, with a view on when each is most appropriate
• Data governance in practice: catalogue, lineage, access control, quality, and Regulatory obligations
• Must have a conceptual appreciation of AI systems and a willingness to learn
• Owned platform cost: sizing, forecasting and bringing a runaway bill back down
• Estimating and scoping work, and shaping proposals that were won
• SC clearance, or eligibility for it

Desirable

• You’ll be the architect on our highest-impact data engagements
• You will define the vision with senior clients, defend your design in front of CTOs and engineering teams, and stay grounded in the codebase to prove it works
• When the project demands it, you’ll step up as Tech Lead
• Off-project, you’ll help shape our practice: taking real-world learnings and building the reference architectures, repeatable propositions, and delivery standards that set the benchmark for everything we do next
• Lead the architecture work on client engagements from discovery through to production
• Run discovery and target-architecture workshops with client CTOs, heads of data and engineering leads
• Translate commercial goals into an architecture and a sequenced roadmap the client can fund
• Be the senior technical voice the client trusts when trade-offs, cost or risk are contested
• Set and own the target data architecture: ingestion, storage, transformation, serving, governance
• Make and document the significant decisions (lakehouse vs warehouse, batch vs streaming, build vs buy) with the trade-offs written down
• Define non-functional requirements, cost models and FinOps guardrails alongside the functional design
• Build in security, data protection, lineage and quality from the start, not as a later workstream
• Take the tech lead role on engagements when the shape of the team requires it
• Write and review code where it matters: reference implementations, spikes, thorny pipelines, infrastructure-as-code
• Set engineering standards for the practice — testing, CI/CD, environments, code review, observability
• Coach and unblock engineers; raise the standard of the people around you
• Spot and qualify follow-on work inside live accounts
• Build reusable assets: reference architectures, accelerators, delivery playbooks
• Help define the hiring bar and interview the next data engineers and architects
• Work with the existing cloud infrastructure and testing practices so data engagements pull them through, and vice versa