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.
About The Role
Data 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. To do this well, we need someone who understands how proper data architecture is built, how it behaves in the real world, and how to bring structure and clarity to a growing ecosystem of products and services.
As Head of Data Engineering, you will shape the direction of our data platform and take responsibility for the systems that move, process and safeguard our data. You will work closely with engineering, AI and product teams to ensure the whole organisation can rely on a consistent and trustworthy data foundation. This is a technical leadership role for someone who enjoys the challenge of building a platform that is clean, scalable, and designed for long term use rather than short term convenience.
Key Responsibilities:
- Set the direction for our data platform, covering ingestion, transformation, warehousing and governance.
- Build and lead a strong data engineering team capable of supporting both real time and batch workloads.
- Design data architectures that serve multiple products, including AI models, real time scoring systems and analytical tools.
- Establish clear data standards, schemas and contracts so teams can integrate with confidence.
- Put in place robust data quality checks, validation steps and reconciliation processes to ensure information is accurate and consistent.
- Work closely with AI teams to provide reliable feature pipelines and training data, and to support inference workflows.
- Collaborate with backend and platform teams to integrate event streams, APIs and operational data into the platform.
- Implement proper observability across the data stack, including lineage, freshness, completeness and performance.
- Make informed decisions on tooling across storage, orchestration, streaming, and transformation.
- Oversee documentation and architectural guidelines to keep the platform coherent as it grows.
- Support product and partner teams when data from casinos or external systems needs to be integrated.
- Maintain awareness of infrastructure costs and design choices that keep the platform sustainable in the long run.
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.
About You:
- Strong experience leading data engineering teams and delivering production data platforms that support real products, not just dashboards.
- Comfortable designing and operating data pipelines and warehouses that serve both operational systems and analytical or modelling needs.
- Good understanding of streaming technologies, batch processing and cloud native data tooling.
- Practical experience working with machine learning teams or directly with ML pipelines, including feature generation, training data preparation and model serving.
- Familiar with ML operations in a real setting, such as monitoring model inputs and outputs, handling data drift, and keeping training and production environments in sync.
- Hands on experience with workflow orchestrators such as Temporal, Airflow or Dagster, ideally across both data and ML workflows.
- Deep knowledge of SQL and at least one modern data processing engine or framework.
- Comfortable with tools that support data quality, lineage, metadata and observability across both data and model pipelines.
- Able to work closely with AI, backend, product and analytics teams and explain technical decisions in clear language.
- Confident in architectural decision making, setting standards, and mentoring engineers at different levels.
- Someone who cares about clarity, reliability and thoughtful engineering, and who understands the trade-offs between speed, cost and long-term maintainability.
