Data Analytics & Engineering Tech Lead - IoT in Kraków

Data Analytics & Engineering Tech Lead - IoT Function: Digital & Technology

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

Data Analytics & Engineering

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.

You are a perfect match if you also have:

AWS Step Functions

EventBridge or event‑driven data processing

Lambda‑based orchestration or lightweight integration patterns

Iceberg, Delta, or similar table formats

understanding of Azure data platform concepts

working with Azure Data Lake, Azure Data Factory, Synapse, Fabric, or similar services

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

experience supporting advanced analytics teams

experience with operational dashboards or Grafana

experience with KPI and metric standardisation across multiple sites or OpCos.

At HEINEKEN Kraków, we take integrity and ethical conduct seriously. If someone has concerns about a possible violation of legal regulations indicated in Polish Whistleblowing Act or our Code of Business Conduct, we encourage them to speak up . Cases can be reported to global team or locally (in line with the local HBSP Whistleblowing procedure) by selecting proper option in this tool or by communicating it on hotline. #LI-HYBRID