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About the Role

You will be the architect on our highest-impact data engagements, defining the vision with senior clients, defending your design, and staying grounded in the codebase. You will also step up as Tech Lead when needed and contribute to shaping the practice by developing reference architectures and delivery standards.

Responsibilities

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
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

Requirements

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
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
Data modelling depth across dimensional, Data Vault and lakehouse/medallion approaches
Data governance in practice: catalogue, lineage, access control, quality, and Regulatory obligations
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
Databricks and/or AWS certification at professional level
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
ML 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
Opinionated but not dogmatic: recommends the simplest architecture that meets the requirement, and says so when a client's preferred tool is the wrong one
Comfortable being the only data person in the room and building the case from scratch
Writes clearly — decision records, options papers and diagrams a client executive can act on
Commercially aware: understands utilisation, margin and how a scope conversation protects both sides
Generous with knowledge; wants to grow a team rather than be its single point of failure
Happy with the ambiguity of an early-stage practice inside an established business

Skills

Python
SQL
Spark
dbt
Infrastructure-as-code
Terraform
Data modelling
Data governance
Catalogue
Lineage
Access control
Quality
Regulatory obligations
Platform cost management
Estimating
Scoping
Proposal writing
Databricks
AWS
Streaming
Event-driven architecture
Kafka
MSK
Kinesis
Structured Streaming
Data products
Data mesh
Federated ownership models
ML platform
AI platform
Feature stores
MLflow
Model serving
RAG
Agentic use cases
Financial services
Healthcare
Public sector

Location

Hybrid working

Work Type

Hybrid working

Experience Level

10+ years in data engineering and architecture
At least 3 years as the lead architect on complex, multi-team programmes

Education Level

Databricks certification at professional level
AWS certification at professional level

Benefits

25 Days of Annual Leave
1 extra day of annual leave every year for the first three years
Holiday buying scheme
Pension Plan: Employer-matched contributions up to 5%
Health Benefits: Comprehensive coverage for medical, dental, optical, and alternative therapies
Fitness Support: Subsidised gym memberships
Fitness Support: Bike-to-work scheme
Commuter Benefits: Season ticket loans
Free Annual Rail Cards
Electric vehicle salary sacrifice scheme
Social Events
Training & Development
Full support for certifications

About the Company

Scale Factory is an AI enablement consultancy delivering quality-led AI-first foundations.
They embed capability and excellence into the foundational fabric of companies.
They help leaders move faster, take smarter risks, and pursue transformation.
Formerly Ten10, they have deep roots in Quality, ensuring their AI solutions work robustly.
They combine engineering discipline with cutting-edge AI expertise to deliver scalable foundations.