Join to apply for the Data Lead (Modeling focus) role at Apolitical
This range is provided by Apolitical. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.
Base pay range
Overview
* Reporting to: Director of Engineering
* Location: Hybrid, UK-based due to data handling and contractual constraints. 2-3 days per week onsite at our London office (Westminster from Jan 2026).
* Hands‑on vs leadership: ~60% hands‑on / 40% leadership & strategy
* Visa sponsorship: UK national or visa holder preferred, but not a dealbreaker.
* Background checks: Due to the nature of the work we do with global governments and partners, all employees need to pass background checks, verifying your identity, education (if relevant), work history, sanctions, criminal record, adverse financial history and right to work.
* You can expect to hear from us, no matter the outcome, by: 15th December
* Salary expectations: We aim for transparency on salary bands. If our range is misaligned with your expectations, we’d welcome an open conversation as early as possible.
Role
Apolitical’s product runs via a modern TypeScript mono‑repo; our analytics/data stack is centred on BigQuery, dbt Core, Airflow/Airbyte, ThoughtSpot, Google Analytics with CI/CD already in place. You will lead and evolve this stack from “good foundations” to a pragmatic, business‑aligned data platform that a small data squad can operate and scale.
You will:
* Own data strategy & architecture: Set a 6–12 month plan (then extend to 2–3 years) that balances quality, governance, compliance and lineage with business outcomes and small‑team execution. Partner closely with the Director of Engineering (DoE), Data Protection Officer (DPO) and Security.
* Lead a small cross‑functional data squad (2–3 ICs to start) across data engineering, analytics (BI / reporting) and data science; act as tech product manager for the team (backlog, cadence, stakeholder comms).
* Be hands‑on where it matters: data modelling (dbt), pipeline orchestration (Airflow), ingestion (Airbyte), BI (ThoughtSpot), and targeted Python.
This is role has a path to Data Director as the company scales and the data function formalises into an enabling platform team.
What you'll do
1) Strategy & stakeholder partnership
* Translate ambiguous business questions into metrics, models, and SLAs; maintain a living data roadmap aligned to product/company goals.
* Co‑author a 6–12 month data strategy covering resourcing, tech choices, standards, tooling and processes; extend to a 2–3 year horizon as the org scales.
* Partner with DPO/Security to prioritise GDPR‑aligned data protection, privacy‑by‑design, auditability and retention/erasure workflows.
* Design models that respect OLTP vs OLAP trade‑offs; choose normalisation vs denormalisation deliberately for latency, cost, and usability.
* Apply normal forms in RDBMS designs; structure star/snowflake schemas for warehouse marts and BI self‑serve.
* Optimise for columnar stores (partitioning/clustering, pruning); understand differences from classic row‑store RDBMS.
* Nice to have: graph stores (entity/relationship insights) and vector stores (chunking, semantic search, retrieval patterns) and when not to use them.
* Round out with pragmatic patterns: SCDs, CDC, event/date modelling, data vault vs dimensional trade‑offs, cost/perf governance in BigQuery.
* Evolve ingestion (Airbyte) and transformation (dbt) pipelines and their contracts/tests; improve observability and alerting (dbt tests, Great Expectations).
* Own Airflow DAGs (scheduling, retries, backfills), including environment sync/restore workflows that keep analytics reliable and testable.
* Establish a governed semantic/metrics layer so KPIs mean the same thing everywhere; enable ThoughtSpot self‑serve and performance‑aware dashboards.
4) Governance, compliance & lineage
* Implement data classification, access controls, lineage, and audit within CI/CD; ensure PII handling, masking, and retention match our obligations.
* Align processes with engineering standards (docs, quality rules) and work with platform teams as they formalise.
5) Team leadership & delivery management
* Line‑manage 2–3 ICs, run 1:1s, coach on craft, and evolve progression frameworks for data roles with the Director of Engineering.
* Operate as the team’s tech product manager: maintain the backlog, run an agile cadence (Kanban/Scrum hybrid), and provide transparent stakeholder comms.
* Partner with product/engineering squads in the TS mono‑repo environment and the separate data‑v2 repo to streamline delivery across boundaries.
Expected outcomes (first 12 months)
30–60 days
* Inventory critical sources, PII classes, lineage and SLAs; fix top data quality gaps and publish an initial metrics catalog.
* Agree the 6–12 month data strategy and OKRs with DoE, DPO, Security, and key stakeholders.
90 days
* Land one high‑value domain mart (star/snowflake) with governed metrics in ThoughtSpot and dbt tests; reduce time‑to‑insight for a key KPI by >50%.
* Establish Airflow runbooks/alerts and backfill playbooks and publish a resourcing & hiring plan for the squad.
6 months
* Roll out data contracts in CI, lineage visibility, and privacy controls for priority tables.
12 months
* A small, stable data squad operating an auditable, reliable platform; a living 2–3 year architecture roadmap aligned to company strategy; clear career paths for data roles.
About you (what we will be looking for in CVs)
Must have experience
* Hands‑on stack: BigQuery, Google Analytics, dbt Core, Airflow, Airbyte, ThoughtSpot (or equivalent BI); strong SQL; Python for light transforms/ops.
* Governance & compliance: data contracts, testing, lineage, access control, and pragmatic GDPR‑aligned practices with DPO/Security partners.
* Leadership: leading a small data team, backlog ownership, agile cadence, stakeholder management; clear written communication.
Let us know if you have…
* Graph data stores (modelling, traversals, when they beat joins).
* Vector stores and retrieval patterns (chunking, semantic search, RAG‑style use cases).
* Exposure to ML/MLOps (feature stores, experimentation) and cost/performance governance in BigQuery.
Don't meet every single expectation? Studies have shown that women and people of colour are less likely to apply to jobs unless they meet every single qualification. Apolitical is dedicated to building a diverse and inclusive workplace, so if you're excited about this role but your past experience doesn't align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.
Seniority level
Mid‑Senior level
Employment type
Full‑time
Job function
Analyst, Engineering, and Product Management
Industries
Technology, Information and Internet
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