{"id":794787,"url":"https://alion.io/job/blocklabs-data-platform-engineer","title":"Data Platform Engineer","company":{"id":670169,"name":"Block Labs","domain":"blocklabs.io","url":"https://alion.io/company/blocklabs-2","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":75,"open_postings":3,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-28T05:59:10Z"}},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":{"label":"European time zones","utc_offset_min":0,"utc_offset_max":2},"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["BG","HR","IE","IT","ME","RO","RS","ES","AL","AM","GE","GR","XK","MT","PT","BA"],"hiring_countries_total":16,"salary":null,"salary_estimate":{"min_usd":30000,"max_usd":80000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1015},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Airflow","optional":false},{"name":"Amazon ECS","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"Apache Kafka","optional":false},{"name":"Apache NiFi","optional":false},{"name":"AWS","optional":false},{"name":"AWS Fargate","optional":false},{"name":"AWS Glue","optional":false},{"name":"CI/CD","optional":false},{"name":"ClickHouse","optional":false},{"name":"CloudFormation","optional":false},{"name":"Druid","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Great Expectations","optional":false},{"name":"Kubernetes","optional":false},{"name":"pySpark","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Amazon S3","optional":true},{"name":"Amazon SageMaker","optional":true},{"name":"DynamoDB","optional":true},{"name":"Feast","optional":true},{"name":"Feature Store","optional":true},{"name":"IAM","optional":true},{"name":"Presto","optional":true},{"name":"Python","optional":true},{"name":"Trino","optional":true}],"status":"live","first_seen_at":"2026-07-20T13:39:27Z","employer_posted_date":"2026-07-20","last_verified_at":"2026-10-28T19:47:53Z","board_verified":true,"closed_at":null,"days_open":100,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":99},"description":"About Block Labs\nBlock Labs is a premier technology studio operating at the bleeding edge of Web3, Artificial Intelligence, and iGaming. We don't just ship features; we engineer high-scale, production-grade platforms that power the next generation of digital products.\nWe are a collective of senior engineers, product strategists, and builders who refuse to compromise on architecture. Whether we are designing autonomous multi-agent AI systems, building decentralized financial infrastructure, or architecting high-frequency iGaming platforms, our standard is excellence.\nWe move fast, but we build for the long term. If you are looking to work alongside a team that values deep technical expertise, thoughtful system design, and product ownership, Block Labs is where you belong.\nThe Role\nData & Intelligence now sits at the centre of several products we are developing, and we need a platform that is both dependable and capable of supporting more advanced intelligence over time.\nThis role reflects that shift. We are designing a new data platform that will act as the backbone for everything from real time decisioning to predictive modelling. As a Data Platform Engineer in the Data Team, you will own the end-to-end real-time pipeline, serving data across a unified analytical warehouse and feature-serving layer. You are not building dashboards. You are engineering the commercial nervous system of a multi-tenant platform designed to scale from one operator to 10x with marginal infrastructure cost.\nKey Responsibilities:\nDesign, build, and maintain scalable data pipelines using AWS Glue (PySpark), or equivalent orchestration and transformation tools.\n\nEngineer and optimise the ClickHouse warehouse for sub-second query performance across all back-offices.\n\nImplement data contracts between back-office and the platform. Onboarding a new operator is a config change, not new tables, topics, or feature views.\n\nBuild the feature-serving layer providing pre-computed features to AI agents at millisecond latency.\n\nIntegrate with third-party databases, back-office APIs, and external systems (CRM, affiliates, acquisition platforms).\n\nEstablish monitoring, alerting, and maintenance procedures including pipeline health checks, freshness monitoring, anomaly detection, and data contract SLA enforcement.\n\nOwn CI/CD and infrastructure-as-code for data workloads.\n\nCollaborate with data scientists, agent engineers, BI developers, and infrastructure teams to translate data requirements into reliable, production-grade pipelines.\n\nAbout You:\n3+ years building and operating production data pipelines at scale, with hands-on experience across both streaming and batch paradigms.\n\nExpertise in Apache Kafka (or Amazon MSK): topic design, consumer group management, offset handling, schema registry operations, and production troubleshooting of lag, rebalancing, and throughput issues.\n\nStrong SQL and warehouse engineering skills: experience with columnar analytical databases (ClickHouse strongly preferred, or similar: Druid, BigQuery, Redshift).\n\nPySpark / Spark Streaming proficiency: writing transformation jobs that normalise, enrich, and enforce business rules on event streams. Experience with AWS Glue, Apache Airflow, or Apache NiFi is a strong plus.\n\nData modelling discipline: ability to design normalised, multi-tenant schemas where tenant isolation is a filter, not a fork. Experience with data contracts and schema governance.\n\nCI/CD and infrastructure-as-code experience: automated testing of data pipelines, version-controlled deployments (CloudFormation, Terraform, or CDK), and familiarity with containerised workloads (ECS Fargate or Kubernetes).\n\nData quality and observability mindset: experience implementing pipeline health monitoring, automated data validation (Great Expectations or equivalent), freshness checks, and anomaly detection.\n\nNice to Have\nExperience in iGaming, online casino, poker, or sportsbook platforms.\n\nExposure to blockchain or crypto-native transaction flows, including on-chain event ingestion, token-denominated accounting, or stablecoin settlement.\n\nComfortable operating in an AWS-native environment (MSK, Glue, S3, DynamoDB, ECS, IAM). You understand serverless tradeoffs and can size infrastructure for cost efficiency.\n\nFeature store experience (SageMaker, Feast, or Tecton) building offline/online feature pipelines that serve ML models at inference time.\n\nPrior work in regulated industries (financial services, gambling, fintech) where data lineage, auditability, and compliance are non-negotiable.\n\nExperience migrating legacy query engines (Athena, Trino, Presto) to modern analytical warehouses with reconciliation frameworks to validate correctness.\n\nHow We Work\nFully remote with asynchronous-first communication. EU time zone overlap is preferred.\n\nSmall, high-autonomy team within the Data function. You report to the Head of Data and co-ordinate with the AI, BI, and Infrastructure Teams.\n\nArchitecture decisions are documented and debated. You will participate in design reviews and own your domain decisions.\n\nWe build for multi-tenant scale from day one. Every pipeline, schema, and contract you ship must absorb a new operator without engineering effort.\n\nOn-call rotation will be established in the run phase. During the build phase, the focus is velocity with quality. No firefighting legacy systems.\n\nWhat kind of culture can I expect?\nMature, mission-driven, and low-ego. We value clarity over noise, outcomes over theatrics, and pace without chaos. 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