Data Platform Engineer
- Role
- Data Engineering
- Experience
- Mid
- Employment
- Full-time
Open to 16 countries. Set where you work from to check your eligibility.
No BS summary
Data platform engineer with 3+ years building production data pipelines across streaming and batch. Must know Kafka/MSK, SQL, PySpark/Spark Streaming, data modelling, CI/CD/IaC, and data observability. Remote role limited to listed European/Balkan/Caucasus countries, with EU timezone overlap preferred.
Core skills
Required skills
Optional skills
About Block Labs
Block 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.
We 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.
We 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.
The Role
Data & 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.
This 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.
Key Responsibilities:
- Design, build, and maintain scalable data pipelines using AWS Glue (PySpark), or equivalent orchestration and transformation tools.
- Engineer and optimise the ClickHouse warehouse for sub-second query performance across all back-offices.
- Implement data contracts between back-office and the platform. Onboarding a new operator is a config change, not new tables, topics, or feature views.
- Build the feature-serving layer providing pre-computed features to AI agents at millisecond latency.
- Integrate with third-party databases, back-office APIs, and external systems (CRM, affiliates, acquisition platforms).
- Establish monitoring, alerting, and maintenance procedures including pipeline health checks, freshness monitoring, anomaly detection, and data contract SLA enforcement.
- Own CI/CD and infrastructure-as-code for data workloads.
- Collaborate with data scientists, agent engineers, BI developers, and infrastructure teams to translate data requirements into reliable, production-grade pipelines.
About You:
- 3+ years building and operating production data pipelines at scale, with hands-on experience across both streaming and batch paradigms.
- Expertise 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.
- Strong SQL and warehouse engineering skills: experience with columnar analytical databases (ClickHouse strongly preferred, or similar: Druid, BigQuery, Redshift).
- PySpark / 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.
- Data 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.
- CI/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).
- Data quality and observability mindset: experience implementing pipeline health monitoring, automated data validation (Great Expectations or equivalent), freshness checks, and anomaly detection.
Nice to Have
- Experience in iGaming, online casino, poker, or sportsbook platforms.
- Exposure to blockchain or crypto-native transaction flows, including on-chain event ingestion, token-denominated accounting, or stablecoin settlement.
- Comfortable operating in an AWS-native environment (MSK, Glue, S3, DynamoDB, ECS, IAM). You understand serverless tradeoffs and can size infrastructure for cost efficiency.
- Feature store experience (SageMaker, Feast, or Tecton) building offline/online feature pipelines that serve ML models at inference time.
- Prior work in regulated industries (financial services, gambling, fintech) where data lineage, auditability, and compliance are non-negotiable.
- Experience migrating legacy query engines (Athena, Trino, Presto) to modern analytical warehouses with reconciliation frameworks to validate correctness.
How We Work
- Fully remote with asynchronous-first communication. EU time zone overlap is preferred.
- Small, high-autonomy team within the Data function. You report to the Head of Data and co-ordinate with the AI, BI, and Infrastructure Teams.
- Architecture decisions are documented and debated. You will participate in design reviews and own your domain decisions.
- We build for multi-tenant scale from day one. Every pipeline, schema, and contract you ship must absorb a new operator without engineering effort.
- On-call rotation will be established in the run phase. During the build phase, the focus is velocity with quality. No firefighting legacy systems.
What kind of culture can I expect? Mature, mission-driven, and low-ego. We value clarity over noise, outcomes over theatrics, and pace without chaos. If you’re one of the smartest minds in your craft and want to build with other experts, you’ll feel at home here.
What you'll do
- Design, build, and maintain scalable data pipelines using AWS Glue (PySpark), or equivalent orchestration and transformation tools.
- Engineer and optimise the ClickHouse warehouse for sub-second query performance across all back-offices.
- Implement data contracts between back-office and the platform so onboarding a new operator is a config change, not new tables, topics, or feature views.
- Build the feature-serving layer providing pre-computed features to AI agents at millisecond latency.
- Integrate with third-party databases, back-office APIs, and external systems such as CRM, affiliates, and acquisition platforms.
- Establish monitoring, alerting, and maintenance procedures including pipeline health checks, freshness monitoring, anomaly detection, and data contract SLA enforcement.
- Own CI/CD and infrastructure-as-code for data workloads.
- Collaborate with data scientists, agent engineers, BI developers, and infrastructure teams to translate data requirements into reliable, production-grade pipelines.
- Participate in design reviews and own domain decisions.
- Build pipelines, schemas, and contracts for multi-tenant scale from day one.
- Join an on-call rotation in the run phase.
What they require
- 3+ years building and operating production data pipelines at scale, with hands-on experience across both streaming and batch paradigms.
- Expertise in Apache Kafka or Amazon MSK, including topic design, consumer group management, offset handling, schema registry operations, and production troubleshooting of lag, rebalancing, and throughput issues.
- Strong SQL and warehouse engineering skills.
- Experience with columnar analytical databases such as ClickHouse, Druid, BigQuery, or Redshift.
- PySpark / Spark Streaming proficiency, including writing transformation jobs that normalise, enrich, and enforce business rules on event streams.
- Data modelling discipline, including ability to design normalised, multi-tenant schemas where tenant isolation is a filter, not a fork.
- Experience with data contracts and schema governance.
- CI/CD and infrastructure-as-code experience, including automated testing of data pipelines and version-controlled deployments.
- Familiarity with containerised workloads such as ECS Fargate or Kubernetes.
- Data quality and observability mindset, including experience implementing pipeline health monitoring, automated data validation, freshness checks, and anomaly detection.
- EU time zone overlap is preferred.
- Comfortable working fully remotely with asynchronous-first communication.
- Comfortable working in a small, high-autonomy team within the Data function.
- Comfortable documenting and debating architecture decisions.
- Preferred: Experience in iGaming, online casino, poker, or sportsbook platforms.
- Preferred: Exposure to blockchain or crypto-native transaction flows, including on-chain event ingestion, token-denominated accounting, or stablecoin settlement.
- Preferred: Comfortable operating in an AWS-native environment and understanding serverless tradeoffs and infrastructure cost efficiency.
- Preferred: Feature store experience building offline/online feature pipelines that serve ML models at inference time.
- Preferred: Prior work in regulated industries such as financial services, gambling, or fintech where data lineage, auditability, and compliance are non-negotiable.
- Preferred: Experience migrating legacy query engines to modern analytical warehouses with reconciliation frameworks to validate correctness.
Benefits
- Fully remote with asynchronous-first communication.
- Small, high-autonomy team within the Data function.
- No firefighting legacy systems.
- Mature, mission-driven, and low-ego culture.
- Culture values clarity over noise, outcomes over theatrics, and pace without chaos.
- Work alongside experts in your craft.
Block Labs is a technology studio operating at the edge of Web3, Artificial Intelligence, and iGaming, building high-scale production-grade platforms for digital products.