Connected Brewery Tech Lead - IoT Data Platform

Digital & Technology Team (D&T) is an integral division of HEINEKEN Business Services Poland (HBSP). We are committed to making Heineken the most connected brewery. That includes digitalizing and integrating our processes, ensuring best-in-class technology, and embedding a data-driven culture. By joining us you will work in one of the most dynamic and innovative teams and have a direct impact on building the future of Heineken!

Would you like to meet the Team, see our office and much more? Visit our website: Heineken (heineken-dt.pl)

We are looking for a Connected Brewery Tech Lead to own the technical direction, production readiness, and capability leadership for the Data Engineering, BI Development, and Data Science chapter supporting the Connected Brewery data product ecosystem.

The role is accountable for setting and governing data and analytics standards, reusable architecture patterns, delivery methods, and production-readiness expectations across a multi-OpCo, multi-brewery, multi-use-case ecosystem. Connected Brewery should be treated as a scalable data product landscape, not as a single delivery stream.

The Tech Lead partners with the Product Owner and leads alignment across squad leads, technical leads, platform teams, breweries, OpCos, and global analytics stakeholders to ensure that delivery decisions support both immediate programme outcomes and reusable enterprise capability.

The role is accountable for defining, managing, and executing the short-, medium-, and long-term technical roadmap of the chapter. This includes capability planning, technical risk management, architecture pattern approval, dependency resolution, and the creation of reusable data product patterns that can scale beyond individual use cases or sites.

The role ensures that data pipelines, BI outputs, semantic-layer inputs, analytical solutions, and machine-learning-ready datasets are scalable, reusable, reliable, observable, secure, supportable, and designed for adoption across multiple breweries and OpCos.

The Tech Lead enables squads to operate effectively within their delivery structures while owning cross-squad standards, architecture patterns, data product governance, production-readiness criteria, and downstream consumption requirements.

The role is centred on building durable data foundations and reusable analytics capabilities across extraction, transformation, storage, quality assurance, integration, deployment, operational support, stakeholder engagement, and capability building across data professionals.

Your responsibilities would include:

Chapter Leadership and People Management

• Leading and line-managing approximately 4–10 Data Engineers, BI Developers, and Data Scientists, including work allocation, coaching, performance management, development planning, and capacity planning
• Ownership of the chapter operating model, capability plan, skill matrix, succession risks, and hiring needs for the Connected Brewery data and analytics capability
• Setting technical expectations for chapter members and ensure consistent engineering, BI, analytics, documentation, and production-support practices
• Developing the technical career growth of chapter members through mentoring, feedback, knowledge sharing, and structured capability building
• Governing vendor and contractor contribution where applicable, ensuring external delivery capacity follows chapter standards and contributes to reusable assets
• Definition and governance of the reusable data product patterns, architecture principles, semantic-layer inputs, BI delivery standards, and analytical delivery methods across breweries, OpCos, and use cases
• Approving key architecture patterns and manage technical trade-offs between tactical squad delivery, long-term scalability, operational supportability, and platform direction
• Leading cross-squad dependency management, delivery prioritisation, technical risk resolution, and alignment across squads, platform teams, breweries, and OpCos
• Ensuring that Connected Brewery evolves as a scalable data product ecosystem with reusable methods, components, templates, and standards that can be adopted beyond individual sites.Scalable Data and Analytics Delivery
• Driving the delivery of scalable data pipelines, curated datasets, semantic-layer inputs, BI outputs, advanced analytics outputs, and machine-learning-ready data assets
• Setting standards for reusable, trusted, observable, supportable, secure, and cost-conscious data products and pipelines
• Ensuring data and analytics delivery is designed for repeated adoption across multiple use cases, breweries, countries, and OpCos
• Owning alignment between AWS-based data engineering patterns and Azure-based semantic-layer, BI, and downstream analytics consumption requirements
• Reducing duplication across reports, datasets, metric definitions, transformation logic, and squad-specific implementations by enforcing common reusable patterns
• Accountability for data quality, lineage, observability, testing, monitoring, documentation, and production-readiness standards across data products and pipelines
• Ensuring technical decisions, dependencies, transformation logic, design assumptions, support models, and downstream impacts are documented and governed
• Accountability for planning and influencing the chapter’s technical resource envelope, vendor and contractor contribution, tooling decisions, cloud consumption, and run-cost optimisation within agreed programme and platform budgets. Contributes to annual and quarterly planning for delivery capacity, platform consumption, support needs, and scale/run investment required to productise Connected Brewery capabilities across breweries and OpCos.

You are a good candidate if you have:

Qualifications

• Minimum 10 years of experience across Data Engineering, Data Platform Engineering, Analytics Engineering, BI Engineering, Data Science Enablement, or similar data and analytics roles
• At least 3 years of technical leadership experience in a complex data, analytics, BI, AI/ML, or platform delivery environment
• Proven experience leading, line-managing, or formally coordinating mixed technical teams, including performance management, coaching, capability planning, and work allocation
• Experience defining standards, reusable methods, architecture patterns, governance practices, and production-readiness expectations across multiple teams, sites, markets, or business units
• Experience managing delivery across multiple stakeholders, squads, technical disciplines, and enterprise platforms

Experience / Skills:

• Working across multiple environments such as development, test, staging, and production
• Designing pipelines that support multiple consumers and use cases
• Understanding how to build scalable and reusable data processing patterns
• Understanding how curated datasets are consumed by BI and analytics teams
• Supporting BI Developers in building reliable reporting and semantic-layer outputs
• Supporting the creation of trusted datasets for BI, dashboarding, and advanced analytics
• Helping reduce duplication across reports, datasets, and squad-specific logic
• Ensuring that reporting and analytics requirements are considered when designing upstream data pipelines
• Supporting alignment between operational reporting, historical reporting, and advanced analytics use cases
• Unit and integration testing for data pipeline, with data lineage and traceability
• Pipeline observability and operational monitoring
• Reconciliation between source and target datasets
• Handling pipeline failures, retries, and support processes
• Ensuring transformation logic and data dependencies are documented
• Supporting production readiness and handover of data products
• Ensuring reporting and analytical outputs are based on trusted and well-understood data

Nice to have:

• AWS Step Functions
• EventBridge or event-driven data processing
• Lambda-based orchestration or lightweight integration patterns
• Iceberg, Delta, or similar table formats
• CI/CD deployment of AWS data engineering workloads
• Terraform-based AWS infrastructure deployment
• Understanding of Azure data platform concepts
• Working with Azure Data Lake, Azure Data Factory, Synapse, Fabric, or similar services
• Preparing data for semantic-layer or reporting consumption
• Supporting BI and analytics teams with curated, trusted, and model-ready datasets
• Understanding how data needs to be structured for Power BI or advanced analytics use cases
• Helping align AWS data preparation with Azure-based consumption patterns
• Understanding the hand-off between data engineering outputs and semantic-layer modelling
• Power BI exposure
• Semantic model / semantic layer experience
• Experience supporting advanced analytics teams
• Experience with operational dashboards or Grafana
• Experience with KPI and metric standardisation across multiple sites or OpCos
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