# Senior Data Engineer [Approvalmax](https://gurify.com/jobs?q=Approvalmax) · North America, Australia · Posted 3 months ago Contract Senior [Data](https://gurify.com/jobs/data) [Apply on the original posting → (opens in a new tab)](https://approvalmax.teamtailor.com/jobs/7821364-senior-data-engineer) ## Job description 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 or Moldova. ### 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. 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 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 - Salary of €85,000 - €95,000, dependent on experience/location - Health & Wellbeing Benefit Stipend - Home Office Setup - one off £500 reimbursement, post probation - 26 days of paid time off - 1 additional day off for your birthday - Service-years recognition financial reward - Regular performance-based compensation reviews - Growing international business with 10,000+ subscribers **Live in Approvalmax’s hiring system.** Read from the company's own applicant tracking system, not reposted from a job board — so it's a real, open requisition rather than an ad that outlived the role. 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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\u0026#39;re scaling quickly.\u003C/p\u003E\u003Cp\u003EThe Role\u003Cbr /\u003EReporting to the Data Platform Lead, you will be a hands-on senior engineer responsible for building and maintaining ApprovalMax\u0026#39;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.\u003C/p\u003E\u003Cp\u003ERemote \u2014 applicants must be based in the UK, Serbia or Moldova.\u003C/p\u003E\u003Ch3\u003EKey Responsibilities\u003C/h3\u003E\u003Ch3\u003EPipeline Development \u0026amp; Platform Engineering\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003EDesign, build, and maintain scalable ELT pipelines, ingestion processes, and transformation layers on Azure Data Lake Gen2 \u002B Databricks.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EOwn 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.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EWrite production-grade Python for orchestration, custom ingestion, and data transformation logic; treat pipeline code with the same rigour as application code.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EInvestigate and resolve pipeline failures within agreed SLAs; lead root-cause analysis and implement durable fixes rather than one-off patches.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EOptimise pipeline performance and Databricks compute usage; surface cost and performance opportunities to the Data Platform Lead.\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003EData Quality, Testing \u0026amp; Observability\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003EImplement and maintain data quality frameworks (dbt tests, Great Expectations, or equivalent) across the platform; ensure critical data assets have explicit quality contracts.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EInstrument pipelines with monitoring, alerting, and lineage so issues are detected before they reach consumers.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EDefine and enforce testing standards for ingestion jobs and dbt models: unit tests, integration tests, and freshness/volume/schema checks.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EContribute 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.\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003EData Contracts \u0026amp; Source-of-Truth Stewardship\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003EPartner with Product Engineering, RevOps, and Finance to define and maintain data contracts; ensure upstream changes are reflected before downstream impact.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EContribute to the Central KPI \u0026amp; Metrics Glossary from a data lineage perspective: make it unambiguous which systems feed which metrics and how each is computed.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EBe 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.\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003EAnalytics Enablement \u0026amp; Hub-and-Spoke Support\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003EProvide robust, well-documented data models and tooling that allow embedded (spoke) analysts to work independently without re-deriving core logic.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EPair with analysts on complex modelling problems; help them level up on dbt, SQL performance, and semantic layer design.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EChampion 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.\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003EAI/ML \u0026amp; Agentic Readiness\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003EBuild data assets to be agentic-ready by default: clean semantic layers, consistent metadata, documented contracts that AI agents and LLM tools can reliably consume.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EContribute to the technical foundations for AI/ML initiatives: ingestion of training data, feature pipelines, evaluation datasets, and inference logging.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003ESupport delivery of natural-language interfaces to ApprovalMax\u0026#39;s data (e.g. text-to-SQL, LLM-powered analytics chatbot) by ensuring the underlying data models are query-friendly and well-described.\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003EEngineering Standards \u0026amp; Technical Leadership\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003EAct 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.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EContribute to Architecture Decision Records (ADRs); ensure significant technical choices are documented, justified, and revisable.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EMaintain and improve CI/CD pipelines for dbt models and ingestion jobs; enforce environment promotion discipline (dev -\u0026gt; staging -\u0026gt; prod).\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EMentor more junior engineers and analysts informally: code review, pairing, and lifting the technical bar across the team.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EContribute to the visible technical debt backlog; advocate clearly for capacity to address debt alongside feature delivery.\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003EEssential Key Skills\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003E5\u002B years of hands-on data engineering experience building and operating production data platforms in a SaaS or B2B product environment.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EDemonstrated 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.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EStrong hands-on expertise with cloud-native data platforms, ideally Azure (Data Lake Gen2, Databricks). Equivalent depth on AWS or GCP stacks will be considered.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EExpert-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).\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EStrong Python skills and hands-on experience with workflow orchestration (Airflow, Prefect, Databricks Workflows, or similar); comfortable writing and maintaining production pipeline code.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EExperience defining and consuming data contracts in collaboration with Product and Engineering teams.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003ETrack record of raising engineering maturity in data functions: CI/CD for data pipelines, testing standards, environment promotion, observability, and incident response.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EComfortable being on-call for the data platform and owning incidents end-to-end.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EStrong written communication: able to document architecture, write ADRs, and explain trade-offs to non-technical stakeholders.\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003ENice to Have\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003EExperience contributing to AI/ML infrastructure: feature stores, vector stores, model-serving layers, or evaluation pipelines.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EFamiliarity with LLM application patterns: RAG, text-to-SQL, prompt and response logging, agent orchestration frameworks.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EExperience working within a hub-and-spoke or embedded analytics operating model.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EPrior experience as a lead engineer or tech lead on a small data team, even without direct reports.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003ESaaS or B2B product company background with exposure to product analytics and GTM data.\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003EWhat We Offer\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003ESalary of \u20AC85,000 - \u20AC95,000, dependent on experience/location\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EHealth \u0026amp; Wellbeing Benefit Stipend\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EHome Office Setup - one off \u0026#163;500 reimbursement, post probation\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003E26 days of paid time off\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003E1 additional day off for your birthday\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EService-years recognition financial reward\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003ERegular performance-based compensation reviews\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EGrowing international business with 10,000\u002B subscribers\u003C/li\u003E\u003C/ul\u003E","identifier":{"@type":"PropertyValue","name":"Gurify","value":"senior-data-engineer-at-approvalmax-054f0ff630d2"},"url":"https://gurify.com/job/senior-data-engineer-at-approvalmax-054f0ff630d2","datePosted":"2026-05-29","validThrough":"2026-11-04T23:59:59Z","hiringOrganization":{"@type":"Organization","name":"Approvalmax","sameAs":"https://approvalmax.teamtailor.com"},"directApply":false,"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressCountry":"AU","addressLocality":"North America"}},"employmentType":"CONTRACTOR"} ``` ```json {"@context":"https://schema.org/","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Jobs","item":"https://gurify.com/jobs"},{"@type":"ListItem","position":2,"name":"Australia","item":"https://gurify.com/jobs/australia"},{"@type":"ListItem","position":3,"name":"Senior Data Engineer","item":"https://gurify.com/job/senior-data-engineer-at-approvalmax-054f0ff630d2"}]} ```