Block Labs

Web3, Artificial Intelligence, iGaming

DataPlatformEngineer

€45–65k ~AI est. Portugal FULL TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“Data Platform Engineer at Block Labs. Skills: Data pipelines, Data warehousing, Real-time data, Feature serving. Design scalable data pipelines. Build scalable data pipelines”

What You'll Achieve.

Support 10x scale; Marginal infrastructure cost; Sub-second query performance; Millisecond latency features; Config change onboarding; Enforce business rules; Serve ML models

Industry & Context.

Web3, Artificial Intelligence, iGaming
Problems you'll solve

Production troubleshooting; Lag issues; Rebalancing issues; Throughput issues; Anomaly detection; Root cause analysis

Eligibility Requirements

On-call rotation

What They're Looking For.

Must Have

3+ years building production data pipelines, Hands-on experience streaming and batch paradigms, Expertise in Apache Kafka, SQL and warehouse engineering skills, PySpark / Spark Streaming proficiency, Data modelling discipline, CI/CD and infrastructure-as-code experience, Data quality and observability mindset

Nice to Have

Experience in iGaming, Exposure to blockchain or crypto-native, Comfortable operating in AWS-native environment, Feature store experience, Prior work in regulated industries, Experience migrating legacy query engines

What You'll Do.

Design scalable data pipelines

Build scalable data pipelines

Maintain scalable data pipelines

Engineer ClickHouse warehouse

Optimise ClickHouse warehouse

Implement data contracts

Build feature-serving layer

Integrate with third-party databases

Integrate with back-office APIs

Integrate with external systems

Establish monitoring procedures

Establish alerting procedures

Establish maintenance procedures

Own CI/CD for data workloads

Own infrastructure-as-code for data workloads

Collaborate with data scientists

Collaborate with agent engineers

Collaborate with BI developers

Collaborate with infrastructure teams

Translate data requirements

Ship pipelines for multi-tenant scale

How You'll Work.

Team & Collaboration

Data Team; AI Teams; BI Teams; Infrastructure Teams; Design reviews

Full Job Description

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 age

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