{"id":2891755,"url":"https://alion.io/job/ten10-principal-data-consultant-aws-databricks","title":"Principal Data Consultant (AWS / Databricks)","company":{"id":2048222,"name":"Ten10","domain":"ten10.com","url":"https://alion.io/company/ten10-com","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Welcome to the Jungle","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["London, United Kingdom"],"countries":["GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":110000,"max_usd":192000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":34},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Kinesis","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Data Vault","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"Feature Store","optional":false},{"name":"MLFlow","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false}],"status":"closed","first_seen_at":"2026-10-20T00:11:50Z","employer_posted_date":"2026-10-20","last_verified_at":"2026-10-22T02:39:07Z","board_verified":false,"closed_at":"2026-10-22T02:39:07Z","days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"Join our team as a Principal Data Consultant, where you will 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. You will lead the architecture work on client engagements, run discovery and target-architecture workshops, and set and own the target data architecture. Additionally, you will coach and unblock engineers, build reusable assets, and help define the hiring bar.\nMissions\nLead the architecture work on client engagements from discovery through to production, translating commercial goals into a sequenced roadmap.\nDefine 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.\nShape the practice by taking real-world learnings and building reference architectures, repeatable propositions, and delivery standards.\nProfil recherché\n - SC clearance, or eligibility for it- 10+ years in data engineering and architecture, with at least 3 as the lead architect on complex, multi-team programmes\n- Track record in a consultancy, systems integrator or other client-facing setting - comfortable being accountable to a paying client, not only an internal stakeholder\n- Still writes production-grade code: strong Python and SQL, Spark, dbt or equivalent, infrastructure-as-code (Terraform preferred)\n- Estimating and scoping work, and shaping proposals that were won\n- Designed and delivered modern data platforms end to end on AWS and/or Databricks\n- Data modelling depth across dimensional, Data Vault and lakehouse/medallion approaches, with a view on when each is most appropriate\n- Must have a conceptual appreciation of AI systems and a willingness to learn\n- Data governance in practice: catalogue, lineage, access control, quality, and Regulatory obligations\n- Owned platform cost: sizing, forecasting and bringing a runaway bill back down\n- 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)\n- Streaming and event-driven architecture in production (Kafka/MSK, Kinesis, Structured Streaming)\n- Data products, mesh or federated ownership models applied on a real programme, not just in theory\n- ML and AI platform work: feature stores, MLflow, model serving, and the data foundations behind RAG or agentic use cases\n- Regulated-sector exposure - 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