{"id":875659,"url":"https://alion.io/job/thedotcollective-data-engineer-azure","title":"Data Engineer - Azure","company":{"id":687776,"name":"The Dot Collective","domain":"thedotcollective.com","url":"https://alion.io/company/thedotcollective","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":75,"open_postings":6,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-11-08T05:47:12Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["Warsaw, Poland"],"countries":["PL"],"hiring_countries":["GB","PL"],"hiring_countries_total":2,"salary":null,"salary_estimate":{"min_usd":54000,"max_usd":97000,"period":"year","method":"role_country_seniority_unknown","sample_n":367},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Azure","optional":false},{"name":"CloudFormation","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Amazon SageMaker","optional":true},{"name":"AWS","optional":true},{"name":"CI/CD","optional":true},{"name":"Databricks","optional":true},{"name":"dbt","optional":true},{"name":"Docker","optional":true},{"name":"FastAPI","optional":true},{"name":"Fivetran","optional":true},{"name":"GCP","optional":true},{"name":"JavaScript","optional":true},{"name":"Kubernetes","optional":true},{"name":"MLFlow","optional":true},{"name":"Next.js","optional":true},{"name":"pySpark","optional":true},{"name":"React.js","optional":true},{"name":"Scrum","optional":true},{"name":"Snowflake","optional":true},{"name":"TypeScript","optional":true}],"status":"live","first_seen_at":"2025-08-21T12:40:43Z","employer_posted_date":"2025-08-28","last_verified_at":"2026-11-08T06:22:46Z","board_verified":true,"closed_at":null,"days_open":443,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":442},"description":"About Dot Collective\nWe are a new generation consultancy based across UK and EU and founded on the premises of the engineering excellence and empowering people to make an impact. \nWe work with all modern tech stacks and typically run agile scrum on all our projects.\nAbout you\nAre you passionate about data and its transformational powers? Do you like being able to make a huge difference in a limited period of time? We might be just the right place for you.\nYour key skills and capabilities: \nStrong SQL and Python skills \nData modelling experience (dimensional, relational, or similar) \nExperience building and maintaining backend systems \nFamiliarity with Microsoft Azure \nUnderstanding of APIs, containers, and backend development best practices \nImplementing cloud-native data platforms \nEngineering scalable and reliable pipelines \nGood knowledge of distributed computing with Spark \nUnderstanding of cloud architecture principles and best practices \nHands-on experience in designing, deploying, and managing cloud resources \nAgile ways of working \nExperience in cloud automation and orchestration using tools such as CloudFormation or Terraform \nMonitoring and performance tuning of cloud-based applications and services \nNice to haves: \nExperience with Snowflake, dbt, CubeJS \nExperience with Databricks, PySpark, ML workflows \nExperience with containerized deployments (Docker, Kubernetes, Azure Container Apps). \nFrontend development experience (React, Next.js, TypeScript) \nInfrastructure-as-code (Terraform, CloudFormation) \nModel Deployment & Serving - Deploy and manage ML models using MLflow, Azure ML, SageMaker, or similar, ensuring scalability and performance \nMonitoring & Retraining - Set up model drift detection, performance monitoring, and automated retraining \nML Pipelines & CI/CD - Automate end-to-end ML workflows \nKey tasks: \nDevelop, maintain, and optimise backend services for our SaaS platform \nWork with SQL and Python daily to build robust data pipelines and transformations \nModel, structure, and query data to support product features and reporting \nIntegrate and maintain data ingestion flows (e.g., Fivetran, custom connectors) \nImplement backend APIs (FastAPI) to power our web application \nWork with our cloud environment (Azure - experience with AWS or GCP also valuable) \nCollaborate with teammates to plan, build, and deploy features in an Agile way \nContribute to CI/CD workflows, monitoring, and infrastructure-as-code \nWe expect you to have some knowledge about how to architect, design, develop, deploy, and operate a data platform. \nOur promise to you\nWe will always see you as a human being and will do our very best to support your needs and wellbeing - well-designed co-working and collaboration spaces, remote working patterns that work for you, parenting leave, sabbaticals and ability to work on personal projects.\nWe believe that a geled team is worth its weight in gold - we will do everything we can to avoid breaking well-performing teams. Whilst continuity across every project is not always possible, we thoughtfully assemble high-performing, blended teams with the appropriate levels of experience and expertise to deliver the best outcomes for our clients - and teams you’ll enjoy working with.\nWe are committed to prioritising the wellbeing of our employees. To fulfill this promise, we provide a comprehensive employee wellbeing program that includes mental health support, flexible working arrangements, wellness activities, and a positive work culture.\nWe recognise that the world of tech delivery has moved on significantly in the last 15 years and know a thing or two about how to bring projects over the line without experiencing lots of despair and burn-out. 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