Data Platform Engineer/Architect (Contractor)
About us
Devtech provides digital innovation services that help Fortune 1000 and emerging companies transform, scale and disrupt. We partner with our clients to envision and develop next-gen digital and cloud solutions that drive impactful business outcomes through people and technology.
Our mission is to empower every innovative business in the world to do what they do best, even better.
Founded in 2012, Devtech successfully bootstrapped the business for many years before securing institutional growth capital in 2022 and 2024 to fuel our next stages of growth. We are a team of over 300 professionals across Europe and North America, and our continued growth is a testament to the quality of work our teams produce.
At Devtech, we’re fostering an environment of autonomy, mastery, and purpose, where our team members can grow and thrive. As we continue to scale globally, we're excited to welcome new team members who share our curiosity and growth mindset, and are ready to make an impact!
What you will do
We're looking for one senior engineer to set up a data warehouse from scratch and take it through to the first working reporting layer, then extend that same platform to support AI.
This is deliberately a single, broad role rather than a team. The first phase is foundational engineering: ingestion, modelling, data quality, and reporting people can trust. The second phase opens the platform up to AI, giving models governed access to the data, and building assistant and retrieval capabilities on top.
You'll be the person who makes the early architecture decisions that determine whether phase two needs a rebuild or just an extension.
Phase one — warehouse foundation and reporting
Design and build the data warehouse: modelling that holds up as new source systems are added
Build ingestion architecture: pipelines, quality gates, and orchestration
Own data hygiene: cleansing, deduplication, validation rules, and reconciliation across sources
Define the semantic layer: shared metric and entity definitions the whole business works from
Deliver the first reporting iteration: dashboards, drill-down reporting, and operational KPIs in Power BI
Phase two — AI enablement
AI readiness: governance, access control, and shaping data so models can consume it safely
RAG and agent enablement: embeddings, vector search, document processing, retrieval design
AI roadmap definition: sequencing what's worth building first, from AI-assisted analytics through predictive models to an internal AI assistant
What you will need
Substantial experience designing and delivering data warehouses end to end, across multiple source systems
Experience designing and implementing modern data platforms on AWS (Redshift, Glue, Athena, Lake Formation, S3, QuickSight or equivalent services)
Strong ingestion and orchestration engineering: batch and incremental pipelines, automated quality gates, monitoring
Demonstrable data quality and hygiene work: you've cleaned up messy source data and made it trustworthy, not just moved it
Semantic layer and dimensional modelling experience
BI delivery, ideally Power BI: dashboards, drill-downs, operational KPI reporting
Data governance fundamentals: lineage, access control, PII handling
Practical familiarity with AI data enablement: RAG, embeddings, vector databases, or preparing data for model access
Comfort working directly with business stakeholders, translating vague asks into a sequenced technical plan
Experience migrating data workloads from Azure or other cloud platforms to AWS is a plus.
Feature engineering or ML enablement for forecasting, risk, or inventory planning use cases is a plus.
Natural-language analytics over a governed semantic model is a plus.
Internal AI assistant, agent, or workflow automation delivery is a plus.
Domain exposure to supply chain, logistics, distribution, or food and agriculture is a plus.