{"id":2118968,"url":"https://alion.io/job/investengine-analytics-engineer","title":"Analytics Engineer","company":{"id":3707,"name":"Investengine","domain":"investengine.com","url":"https://alion.io/company/investengine","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Teamtailor","truth_index":null},"role":"Analytics","role_family":"Analytics","seniority":null,"employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["London, United Kingdom"],"countries":["GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":53000,"max_usd":112000,"period":"year","method":"role_country_seniority_unknown","sample_n":208},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Redshift","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Copilot","optional":false},{"name":"dbt","optional":false},{"name":"GitHub","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Least Privilege","optional":false},{"name":"Looker","optional":false},{"name":"Metabase","optional":false},{"name":"Notion","optional":false},{"name":"Python","optional":false},{"name":"Slack","optional":false},{"name":"SQL","optional":false},{"name":"Asana","optional":true}],"status":"live","first_seen_at":"2026-09-07T16:40:37Z","employer_posted_date":"2026-09-07","last_verified_at":"2026-10-16T07:51:18Z","board_verified":true,"closed_at":null,"days_open":38,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":38},"description":"About InvestEngine\nWe’re a ~200-person, AI-first fintech. We move fast, we expect people to own things end to end, and we don’t have a lot of specialist support infrastructure to hide behind - if you spot a gap, you’re expected to close it, not escalate it and wait.\nAbout the role\nWe’re hiring our first dedicated Analytics Engineer at InvestEngine. This is a foundational hire: you’ll sit at the intersection of data engineering, analytics engineering, and business partnership - turning raw, disparate data into governed, trustworthy models that the rest of the company makes decisions on.\nYou won’t just write dbt models in isolation. You’ll work directly with stakeholders across the business - Product, Engineering, Risk, Finance, Investment, Marketing - to understand the problems they’re actually trying to solve, then design and ship the pipelines, models, and semantic layer that solve them. You’ll also help shape how we use AI-enabled tooling to make the whole analytics function faster, and how we structure our data so that AI tooling can be trusted to answer questions about it.\nWe’re actively modernising our data stack, and we want the person in this role to shape it. Some of it is in place, some of it is being rebuilt, and some of it hasn’t been decided yet. If you have informed opinions about orchestration, ingestion, testing, semantic layers, or BI - we want to hear them.\nWho thrives here\nWe’ve pitched this at mid to senior level, but the honest position is that the level matters less than the appetite. The ultimate test is hunger, and the desire to shape this space and have real impact. If you’re technically strong but hungrier than your years of experience suggest, apply anyway - we’d rather have someone who wants to own this than someone who has simply done it before somewhere bigger.\nBeyond that, this role suits someone who:\nOperates well with ambiguity and limited structure. You’ll be the first analytics engineer here - no existing playbook, no dedicated data engineering team to lean on, no established BI function to slot into. You’ll be building a lot of it as you go.\n\nWants a small, fast company on purpose - not just escaping bureaucracy elsewhere, but genuinely drawn to the trade-off: more ownership and visibility, less process and hand-holding.\n\nLives in the tools, not in email chains. You default to GitHub, dbt, Slack/Notion, and automation over status meetings and Word docs.\n\nCan point to things they’ve actually built and shipped, not just designs or concepts they contributed to.\n\nAlready uses AI to do the job better - Claude, Copilot, or similar - and is curious about where it can go further.\n\nLearns fast. We care more about how quickly you close a knowledge gap than how complete your CV looks today.\n\nWorks well directly with Product, Marketing, Operations, and Engineering, not just with other analysts - this role sits right at that boundary.\n\nOur values\nEveryone at InvestEngine is expected to live these, and this role will be assessed against all five:\nAct like an owner - you take responsibility for outcomes, not just tasks\n\nKeep improving. Stay curious. - you push your own standards and the team’s, and you’re genuinely curious about better ways to work\n\nAchieve more together - you make the people around you (business stakeholders included) more effective, not just yourself\n\nSpeak up. Share openly. - you flag risks, disagreements, and better ideas rather than sitting on them\n\nPut customers first. Create real value. - you build things because they solve a real problem, not because they’re interesting to build\n\nWhat you’ll do\nAnalytics engineering & the semantic layer\nDesign, build, and maintain dbt Core models that transform raw data into clean, well-tested, well-documented datasets\n\nOwn and evolve our semantic layer - consistent metric definitions, business logic, and naming that the whole company can trust and query against\n\nEstablish and enforce data modelling standards, testing practices, and documentation as the function scales\n\nEvaluate and help roll out modern BI tooling - experience with Lightdash, Omni, Looker, or Metabase all translates well - to make metrics genuinely self-serve for non-technical stakeholders\n\nBuilding the AI context layer\nStructure our models, metric definitions, documentation, and lineage so they’re machine-readable, not just human-readable - because our semantic layer is what AI tooling reasons over\n\nMake AI-driven analytics reliable: if two people (or two agents) ask the same business question, they should get the same answer, because the definition lives in one governed place\n\nTreat naming, documentation, and metric definitions as first-class engineering deliverables rather than afterthoughts, since they directly determine how trustworthy our AI-enabled BI is\n\nUse Claude to accelerate development - model scaffolding, code review, documentation, QA, and pipeline debugging - and Notion AI to keep documentation and runbooks current and genuinely useful\n\nBring a point of view on where AI can responsibly speed up analytics engineering, and help the wider team adopt it well\n\nData engineering & the platform\nBuild and maintain ingestion and transformation pipelines across our stack. We use a modern data stack, such as AWS, Redshift, and Airflow\n\nWrite production-quality Python for extraction, transformation, and automation tasks that fall outside dbt’s remit\n\nHelp us modernise: we’re actively evolving this stack and expect you to challenge and improve it, not just operate it\n\nWarehouse cost & performance\nOwn the performance and cost profile of our Redshift warehouse - you should be able to read a query plan, choose sensible sort and distribution keys, and design incremental models without being asked\n\nSpot and act on opportunities to reduce complexity and cost across our models and pipelines, rather than letting spend and technical debt accumulate quietly\n\nMake deliberate trade-offs between freshness, cost, and complexity - and explain those trade-offs to the business in terms they care about\n\nData quality & observability\nChampion analytics engineering best practice across the company: testing, version control, code review, CI, and documentation applied to data the same way engineering applies them to software\n\nBuild and own data quality, freshness, and pipeline monitoring so problems are caught by us before they’re caught by a stakeholder looking at a dashboard\n\nSet the standard for what “trustworthy data” means here, and hold the line on it as the volume of models and requests grows\n\nUpstream influence\nWork with our engineering teams to make sure application and event design accommodates reporting and analytics requirements before data reaches the warehouse - good analytics starts at the source, not in the transformation layer\n\nInfluence payload and schema design for new services and product features, so we’re not permanently reverse-engineering business meaning out of operational tables\n\nSupport analysts and business users with automation, tooling, and data engineering expertise, and provide training and guidance on how to use our data well\n\nBusiness partnership\nWork directly with stakeholders to understand their problems, not just their requested outputs - translate ambiguous business questions into pipeline and modelling requirements\n\nAct as a trusted advisor on what’s possible with our data, and push back constructively when a request doesn’t solve the underlying problem\n\nSupport financial, operational, and regulatory reporting needs appropriate to a regulated investment platform\n\nGovernance\nApply our data classification and access model when onboarding sources: identify PII (including in free-text fields), decide what belongs in the sanitised analytics layer versus a restricted dataset, and document the outcome\n\nImplement least-privilege access across datasets, reports, and dashboards\n\nWhat we’re looking for\nMust-haves\nHands-on production experience with dbt (we run dbt Core) - this is a hard requirement\n\nStrong SQL expertise\n\nSolid Python for data engineering and automation\n\nWorking knowledge of AWS data services\n\nExperience with GitHub and GitHub Actions (or equivalent CI/CD tooling) for testing and deploying data code\n\nExperience with a modern BI tool (e.g., Lightdash, Omni, Looker, or Metabase), and a genuine interest in semantic layers (e.g., MetricFlow, Cube Core) and self-serve analytics\n\nComfortable using AI tools as part of your day-to-day engineering workflow\n\nStrong communication - you can sit with a non-technical stakeholder, understand their real problem, and explain trade-offs in plain language\n\nOwnership: you’re comfortable being the first and only person in this role, and building things properly from scratch\n\nNice-to-haves\nAirflow or similar orchestration tools, and familiarity with data ingestion tools such as dlt or Airbyte\n\nWarehouse cost and performance tuning, particularly on Redshift\n\nFintech, investment management, or another regulated industry\n\nHandling PII responsibly in an analytics environment - sanitised vs. restricted datasets, role-based access tiers, automated PII detection\n\nExperience with Notion AI or Asana automations\n\nDay-to-day working style\nWe favour clear thinking and simple, well-tested solutions over cleverness\n\nWe expect you to engage directly with the business, not hide behind tickets - understanding the “why” behind a request is part of the job\n\nWe’re genuinely trying to use AI to work faster and better, not as a buzzword - we want people who’ll experiment, share what works, and level up the team\n\nAs the first analytics engineer, you’ll define what “good” looks like here: process, standards, and tooling included\n\nWe believe data should be as accessible as it can safely be, and as restricted as it needs to be - good judgement applied consistently, not blanket lockdown\n\nPractical details\nReports to: Chief Data Officer\n\nTeam: Data & Analytics\n\nLevel: Mid to Senior\n\nLocation / working pattern: London / hybrid","description_format":"text","description_chars":9984,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United Kingdom","iso":"GB","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Investing Apps & Robo-Advisors"],"lifecycle":[{"event":"open","at":"2026-10-08T23:51:19Z"}],"visa":[{"country":"GB","licensed_sponsor":true,"evidence":"Licensed UK visa sponsor (Skilled Worker)","filings_12m":null,"filings_prev_12m":null,"green_card_filings_12m":null,"median_offered_wage_usd":null,"route":"Skilled Worker","cap_exempt":false,"checked_at":"2026-10-16T06:21:30+00:00","sources":["UK Home Office: register of licensed sponsors (workers)"],"filings_for_role_12m":0}],"liveness":{"score":23,"band":"cold","label":"Long shot","p_open":1,"p_active":0.516,"p_room":0.45,"age_days":38,"expected_fill_days":21,"reasons":["conf:3","velocity","win:tail"],"computed_at":"2026-10-16T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/investengine-analytics-engineer","json_url":"https://alion.io/job/investengine-analytics-engineer.json","meta":{"generated_at":"2026-10-16T10:33:26Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":8925,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-17T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":3707},"rest":"https://alion.io/mcp/rest/get_company?id=3707"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Finvestengine-analytics-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Finvestengine-analytics-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Finvestengine-analytics-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/investengine-analytics-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Finvestengine-analytics-engineer"}]}