Principal Data Consultant; AWS​/Databricks

• 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 Fin Ops 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

SC clearance, or eligibility for it10+ years in data engineering and architecture, with at least 3 as the lead architect on complex, multi-team programmes

Track record in a consultancy, systems integrator or other client-facing setting - comfortable being accountable to a paying client, not only an internal stakeholder

Still writes production-grade code: strong Python and SQL, Spark, dbt or equivalent, infrastructure-as-code (Terraform preferred)
Estimating and scoping work, and shaping proposals that were won Designed and delivered modern data platforms end to end on AWS and/or Databricks Data modelling depth across dimensional, Data Vault and lakehouse/medallion approaches, with a view on when each is most appropriate

Must have a conceptual appreciation of AI systems and a willingness to learn

Data governance in practice: catalogue, lineage, access control, quality, and Regulatory obligations

Owned platform cost: sizing, forecasting and bringing a runaway bill back down Databricks and/or AWS certification at professional level, and experience supporting a firm’s partner status (competency badges, certified-headcount targets, co-sell with partner account teams)
Streaming and event-driven architecture in production (Kafka/MSK, Kinesis, Structured Streaming)
Data products, mesh or federated ownership models applied on a real programme, not just in theoryML and AI platform work: feature stores, MLflow, model serving, and the data foundations behind RAG or agentic use cases

Regulated-sector exposure - financial services, healthcare, public sector - and the assurance that comes with it