> Markdown version of [/jobs/ext/1938276-data-platform-engineer](https://www.wearedevelopers.com/jobs/ext/1938276-data-platform-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Data Platform Engineer

- **Company:** Block Labs
- **Location:** Málaga, Spain (Remote available)
- **Contract:** Permanent contract
- **Skills:** Query Performance, Adobe InDesign, Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Amazon S3, Audit Trail, Automation of Tests, Business Intelligence Development, BigQuery, Databases, Continuous Integration, Data Validation, Data Infrastructure, Amazon DynamoDB, Identity and Access Management, Machine Learning, Operational Databases, Blockchain, Standard Sql, SQL Databases, Data Streaming, Multi-Agent Systems, Cloudformation, Pyspark, Kubernetes, Data Lineage, Druid, AWS Glue, AWS Fargate, Apache Kafka, Apache Nifi, Spark Streaming, Web3.js, Presto, Vertica, Terraform, Data Pipelines, Serverless Computing, Legacy Systems, Amazon Redshift
- **Published:** August 5, 2026
- **Apply:** https://www.buscojobs.com.es/data-platform-engineer-en-malaga-ID-366073997

## About the Role

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.

## Description

About Block LabsBlock 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 RoleData & 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 ResponsibilitiesDesign, 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 You3+ 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 HaveExperience 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 WorkFully 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.#J-*****-Ljbffr


## Related Videos

- [How building an industry DBMS differs from building a research one](https://www.wearedevelopers.com/videos/768-how-building-an-industry-dbms-differs-from-building-a-research-one)
- [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data)
- [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning)
- [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail)
- [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works)
- [Swapping a Data Warehouse at Runtime: Zero-Downtime Migration Without Changing a Single Client](https://www.wearedevelopers.com/videos/100311-swapping-a-data-warehouse-at-runtime-zero-downtime-migration-without-changing-a-single-client)

## Related Articles

- [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story)
- [Fully Remote Software Engineer Jobs](https://www.wearedevelopers.com/magazine/447-fully-remote-software-engineer-jobs)
- [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers)
- [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline)
- [Dev Digest 132 - Binging WADFlix?](https://www.wearedevelopers.com/magazine/473-dev-digest-132-binging-wadflix)
- [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)