At a Glance
- Tasks: Build and maintain scalable data pipelines and drive engineering quality across the data stack.
- Company: Fast-growing B2B SaaS company with a global team and innovative software solutions.
- Benefits: Competitive salary, 26 days paid time off, remote work assistance, and performance-based reviews.
- Other info: Opportunity for career growth in a supportive and collaborative environment.
- Why this job: Join a dynamic team and make a real impact on our enterprise data platform.
- Qualifications: 5+ years of data engineering experience and strong Python skills.
The predicted salary is between 70000 - 90000 £ per year.
About ApprovalMax
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 Portugal.
Key Responsibilities
- Pipeline Development & Platform Engineering
- 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.
- Data Quality, Testing & Observability
- 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.
- Data Contracts & Source-of-Truth Stewardship
- 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.
- Analytics Enablement & Hub-and-Spoke Support
- 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.
- AI/ML & Agentic Readiness
- 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.
- Engineering Standards & Technical Leadership
- 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.
Essential Key Skills
- 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.
- 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 Have
- 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.
What We Offer
- Growing international business with 10,000+ subscribers.
- Regular performance-based compensation reviews.
- 26 days of paid time off.
- 1 additional day off for your birthday.
- Remote office assistance.
- Service-years recognition financial reward.
Senior Data Engineer employer: ApprovalMax
ApprovalMax is an exceptional employer that fosters a dynamic and inclusive work culture, where innovation thrives and employees are empowered to make impactful contributions. With a strong focus on professional growth, team members benefit from regular performance-based compensation reviews, generous paid time off, and opportunities to mentor and collaborate with talented colleagues across the globe. Working remotely from the UK, Serbia, Moldova, or Portugal, you'll be part of a fast-growing B2B SaaS company that values your expertise and encourages you to leverage cutting-edge technologies in a supportive environment.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Nail that interview prep! Research ApprovalMax, understand their products, and be ready to discuss how your skills align with their needs. Practise common interview questions and prepare some thoughtful questions to ask them too.
✨Tip Number 3
Show off your projects! If you've got a portfolio or GitHub with relevant work, make sure to highlight it during interviews. It’s a great way to demonstrate your hands-on experience and technical prowess.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in being part of the ApprovalMax team.
We think you need these skills to ace Senior Data Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Senior Data Engineer role. Highlight your experience with data platforms, especially Azure, and showcase your hands-on skills in Python and dbt. We want to see how your background aligns with what we're looking for!
Showcase Your Projects:Include specific projects where you've built or maintained data pipelines. Talk about the challenges you faced and how you overcame them. This gives us a glimpse into your problem-solving skills and technical expertise.
Be Clear and Concise:When writing your application, keep it clear and to the point. Use bullet points for easy reading and make sure to explain your contributions in previous roles. We appreciate straightforward communication!
Apply Through Our Website:Don't forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you're keen on joining our team at ApprovalMax!
How to prepare for a job interview at ApprovalMax
✨Know Your Data Stack
Make sure you’re well-versed in the modern data stack, especially dbt, SQL, and Azure Data Lake Gen2. Brush up on your Python skills too, as you'll need to demonstrate your ability to write production-grade code during the interview.
✨Showcase Your Problem-Solving Skills
Be prepared to discuss specific examples of how you've tackled complex data engineering challenges. Highlight your experience with incident response and how you've implemented durable fixes rather than quick patches.
✨Familiarise Yourself with AI Tools
Since the role involves using AI coding agents, come ready to share how you've integrated tools like GitHub Copilot or Claude Code into your workflow. This will show that you're aligned with their vision of an agentic-ready data platform.
✨Communicate Clearly
Strong written communication is key, so practice explaining technical concepts in a way that non-technical stakeholders can understand. Be ready to discuss how you document architecture and write Architecture Decision Records (ADRs).
