Senior Data Engineer

ApprovalMax

16h•Remote

About The Position

ApprovalMax is a fast-growing B2B SaaS company that helps businesses automate their approval workflows and financial controls. With a global team of over 100 people spanning the UK, Europe, North America, Australia, and South Africa, we build software that matters and we're scaling quickly. The Role Reporting to the Data Platform Lead, you will be a hands-on senior engineer responsible for building and maintaining ApprovalMax's enterprise data platform. You will own the design and delivery of production-grade data pipelines, drive engineering quality across the data stack, and act as a technical mentor for the broader analytics team. As we mature our hub-and-spoke model, you will be a key partner to embedded analysts and a core contributor to making the platform agentic-ready and self-service by default. This is a senior individual contributor role: deep technical work, broad influence, no direct reports. Remote — applicants must be based in the UK, Serbia, Moldova or Portuga l.

Requirements

5+ years of hands-on data engineering experience building and operating production data platforms in a SaaS or B2B product environment.
Demonstrated experience using AI coding agents as a core part of your development workflow, not as a novelty but as a default. You should be able to show how agent-assisted development (Cursor, GitHub Copilot, Claude Code, or equivalent) has materially changed how fast you ship and how you approach technical problems. We are building an agentic-ready data platform, and we need senior engineers who already work this way.
Strong hands-on expertise with cloud-native data platforms, ideally Azure (Data Lake Gen2, Databricks). Equivalent depth on AWS or GCP stacks will be considered.
Expert-level command of the modern data stack: dbt, SQL, dimensional and source-aligned data modelling, semantic layers, and data quality frameworks (Great Expectations, Monte Carlo, or equivalent).
Strong Python skills and hands-on experience with workflow orchestration (Airflow, Prefect, Databricks Workflows, or similar); comfortable writing and maintaining production pipeline code.
Experience defining and consuming data contracts in collaboration with Product and Engineering teams.
Track record of raising engineering maturity in data functions: CI/CD for data pipelines, testing standards, environment promotion, observability, and incident response.
Comfortable being on-call for the data platform and owning incidents end-to-end.
Strong written communication: able to document architecture, write ADRs, and explain trade-offs to non-technical stakeholders.

Nice To Haves

Experience contributing to AI/ML infrastructure: feature stores, vector stores, model-serving layers, or evaluation pipelines.
Familiarity with LLM application patterns: RAG, text-to-SQL, prompt and response logging, agent orchestration frameworks.
Experience working within a hub-and-spoke or embedded analytics operating model.
Prior experience as a lead engineer or tech lead on a small data team, even without direct reports.
SaaS or B2B product company background with exposure to product analytics and GTM data.

Responsibilities

Design, build, and maintain scalable ELT pipelines, ingestion processes, and transformation layers on Azure Data Lake Gen2 + Databricks.
Own the implementation of core data models in dbt: from source-aligned staging through to marts and semantic layers consumed by Power BI, Amplitude, and downstream tools.
Write production-grade Python for orchestration, custom ingestion, and data transformation logic; treat pipeline code with the same rigour as application code.
Investigate and resolve pipeline failures within agreed SLAs; lead root-cause analysis and implement durable fixes rather than one-off patches.
Optimise pipeline performance and Databricks compute usage; surface cost and performance opportunities to the Data Platform Lead.
Implement and maintain data quality frameworks (dbt tests, Great Expectations, or equivalent) across the platform; ensure critical data assets have explicit quality contracts.
Instrument pipelines with monitoring, alerting, and lineage so issues are detected before they reach consumers.
Define and enforce testing standards for ingestion jobs and dbt models: unit tests, integration tests, and freshness/volume/schema checks.
Contribute to incident response: take on-call shifts as part of the rotation, lead post-mortems for incidents you own, and drive action items to closure.
Partner with Product Engineering, RevOps, and Finance to define and maintain data contracts; ensure upstream changes are reflected before downstream impact.
Contribute to the Central KPI & Metrics Glossary from a data lineage perspective: make it unambiguous which systems feed which metrics and how each is computed.
Be the technical owner of critical data domains (e.g. subscriptions, billing, product usage); know them deeply enough to defend the numbers in front of SLT.
Provide robust, well-documented data models and tooling that allow embedded (spoke) analysts to work independently without re-deriving core logic.
Pair with analysts on complex modelling problems; help them level up on dbt, SQL performance, and semantic layer design.
Champion LLM-assisted development across the analytics team: model how to use AI coding tools (Cursor, Claude Code, Copilot, or equivalent) as a default workflow for pipeline and model development.
Build data assets to be agentic-ready by default: clean semantic layers, consistent metadata, documented contracts that AI agents and LLM tools can reliably consume.
Contribute to the technical foundations for AI/ML initiatives: ingestion of training data, feature pipelines, evaluation datasets, and inference logging.
Support delivery of natural-language interfaces to ApprovalMax's data (e.g. text-to-SQL, LLM-powered analytics chatbot) by ensuring the underlying data models are query-friendly and well-described.
Act as a technical authority on the data team: lead design reviews, review pull requests with substance, and be the person analysts and engineers bring hard problems to.
Contribute to Architecture Decision Records (ADRs); ensure significant technical choices are documented, justified, and revisable.
Maintain and improve CI/CD pipelines for dbt models and ingestion jobs; enforce environment promotion discipline (dev -> staging -> prod).
Mentor more junior engineers and analysts informally: code review, pairing, and lifting the technical bar across the team.
Contribute to the visible technical debt backlog; advocate clearly for capacity to address debt alongside feature delivery.