Analytics Engineer (Mid/Senior)

Salary £70,000 - £90,000 (GBP) (top of the mid-level to the top of the senior level)

Team Data

Seniority Mid-level

Closing: 11:59pm, 9th Aug 2026 BST

Perks and benefits:

• Work from home option
• Employee Assistance Programme
• Enhanced maternity and paternity leave
• Paid emergency leave
• Mentoring/coaching
• Salary sacrifice
• Team social events
• Team lunches
• Cycle to work scheme
• Free fruit

Candidate happiness: 8.37 (2157)

Overview:

Reporting to: Head of Data (incoming)

Location: Hybrid, UK-based due to data handling and contractual constraints. 2 days per week onsite at our London office.

Visa sponsorship: UK national or visa holder preferred, but not a dealbreaker.

Background checks: Due to the non-partisan 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. To ensure we maintain high standards of political neutrality, we also perform media and social media checks.

You can expect to hear from us, no matter the outcome, by: 28th August 2026

Salary expectations: We aim for transparency on salary bands. We've set the band to attract experienced mid-level Analytics Engineers or those in a senior IC role. If our range is misaligned with your expectations, we’d welcome an open conversation as early as possible.

Role:

Apolitical builds products for public-sector professionals worldwide. Our core product stack runs in a modern TypeScript mono-repo; our analytics stack centres on Google BigQuery, dbt (Core) for transformation, Airflow / Airbyte / GitHub Actions for orchestration and CI, ThoughtSpot for BI, and Colab/Jupyter notebooks for ideation and analysis. You’ll turn warehouse data into trusted, self-serve insights, define core metrics, and ship dashboards that unblock rapid, data-informed decisions across Product, Growth, Partnerships and Ops.

You’ll be the go-to partner for stakeholders, owning the modelling layer, data quality, and the BI experience end-to-end, while contributing to our Airflow-based pipelines and data contracts that keep analytics reliable.

Tasks and remit:

• Model the warehouse: Design and maintain dbt models (incremental/snapshot), sources, tests, and documentation; enforce naming and folder conventions for scalable analytics.
• Define metrics & semantics: Establish a governed, reusable layer (semantic definitions / metrics catalog) so the same KPI means the same thing everywhere.
• Build business dashboards: Craft ThoughtSpot answers and dashboards for Exec, Product, Growth and Customer teams; automate refresh and distribution.
• Champion self-serve analytics: Craft intuitive star/galaxy schemas for data marts following best practices so analysts and non-technical team members can get what they need.
• Data quality & contracts: Add and maintain tests (dbt + Great Expectations), SLAs and alerting; evolve data contracts in version control and CI.
• Orchestrate & automate: Contribute Airflow DAGs for scheduled loads, dbt runs and backfills; improve observability, retries and alerts.
• Governance & privacy: Handle PII with care; partner with Eng/Legal on compliant data use and auditability across pipelines and BI.
• Stakeholder enablement: Run office hours, write playbooks, and create training resources that reduce ad-hoc analysis load.
• Incident readiness: Help operate and improve our backup/restore and environment-sync workflows to keep analytics resilient.

Timelines vary by onboarding needs, but most team members achieve the following:

Within one month, you will…

• Ship your first dbt model + tests to production and document it in the catalog.
• Publish a high-value dashboard (e.g., weekly growth or funnel view) consumed by one business team.
• Set up your Airflow/dev environment and complete stack onboarding.

Within three months, you will…

• Establish core metric definitions (e.g., activation, engagement, retention) with stakeholders; codify them in models and BI.
• Add quality alerts (dbt tests / Great Expectations) on critical tables; reduce false positives.
• Improve/author at least one Airflow DAG for dbt jobs or backfills; document runbooks.

Within six months, you will…

• Own a domain (e.g., revenue or content analytics) end-to-end with a trusted, self-serve dashboard suite.
• Cut time-to-insight for a key KPI by >50% via modelling and self-serve improvements.
• Propose and deliver a roadmap to evolve our semantic layer / contracts in CI.

About you:

This is a great fit if you…

• Are an Analytics Engineer / BI Engineer with strong SQL and data-modelling fundamentals (star/snowflake, slowly changing dimensions, incremental patterns).
• Have hands-on experience with BigQuery + dbt Core (sources, macros, snapshots, documentation) and are comfortable reviewing SQL/PRs in Git.
• Can design effective dashboards in ThoughtSpot or other BI tools, balancing UX with performance.
• Understand orchestration (Airflow), testing (dbt tests / Great Expectations), and CI for analytics code.
• Communicate clearly with non-technical partners; you enjoy translating ambiguous questions into measurable metrics.

To highlight on your CV…

• Familiarity with Airbyte or other ingestion tools; Python for lightweight transforms; exposure to data contracts in CI.
• Prior work in a modern TypeScript/NodeJS/NestJS environment alongside product squads.
• A pure data scientist or ML researcher; this role focuses on analytics engineering (modelling, quality, BI, orchestration).
• A people manager; this is an IC role with broad cross-team influence.