Senior Data Engineer

(Senior) Data Engineer – D&A Product (Packaging, Brewing & Utilities)

Global Digital & Technology (D&T) – Supply Chain

Role Purpose

As a (Senior) Data Engineer within HEINEKEN's D&A Product Organisation, you play a key role in enabling the Smart Brewery vision. You are embedded in a cross‑functional product squad (Connected Brewery, Packaging, Brewing, or Utilities), working under the functional direction of the Product Lead and in close collaboration with Business Analysts, BI Developers, Data Scientists, and Brewery SMEs.

You transform operational and analytical needs into robust, scalable, and globally reusable data pipelines, data models, and foundational datasets. These assets power diagnostics, reporting, advanced analytics, optimisation, and predictive capabilities across our breweries.

You ensure data is reliable, trusted, performant, and aligned with global D&A standards.

You bring strong engineering craftsmanship, technical leadership, and structured problem‑solving to elevate our global data foundations. Through active engagement in Agile Ways of Working, you ensure every increment delivered by the squad is high‑quality, maintainable, and business‑relevant.

Key Responsibilities

1. Product Discovery & Requirements

• Assess feasibility of new use cases by analysing data availability, structure, quality, and integration needs.
• Translate operational and analytical needs into clear data engineering requirements, data flow designs, and technical tasks.
• Collaborate with Product Leads, Business Analysts, and Developers to shape a structured and prioritised backlog.
• Contribute to the definition and refinement of global, reusable data components and standards aligned with the D&A roadmap.
• Participate in the technical assessment of new ideas and enhancement requests (Idea Portal, SNOW).
• Provide L2 technical support for use case–related queries and incidents, ensuring timely resolution and knowledge development.

2. Stakeholder Engagement

• Communicate data pipeline logic, architecture decisions, and engineering constraints clearly to technical and non‑technical audiences.
• Collaborate closely with Business Analysts, SMEs, and BI Developers to ensure KPI logic, data semantics, and models align with available data and technical reality.
• Support alignment sessions on data structures, data flows, governance rules, and metric definitions.
• Act as a (senior) engineering voice ensuring solutions are high‑quality, future‑proof, and operationally relevant.

3. Data & Analytics Enablement

• Design, build, and operate scalable, serverless data pipelines using AWS and Azure technologies (e.g., Lambda, Glue, Step Functions, S3, DynamoDB, Aurora).
• Develop and maintain Python ETL packages, including logging, lineage, validation, transformation logic, and reusable components.
• Implement high‑quality data models and semantic layers aligned with HEINEKEN D&A standards.
• Partner with BI Developers to provide the right data structures for dashboards, KPIs, semantic models, and analytical products.
• Identify data quality issues and collaborate with Business Analysts and SMEs to investigate, fix, and prevent reoccurrence.

4. Product Delivery & Value Realisation

• Deliver high‑quality, fully tested engineering increments meeting acceptance criteria and the squad's Definition of Done.
• Contribute to the engineering development lifecycle: build → test → validate → document.
• Support sprint reviews and demos by explaining pipeline logic, data behaviour, and architectural decisions.
• Monitor pipeline reliability, data freshness, and system performance; proactively recommend improvements.
• Contribute to enhancement and demand assessment by providing technical impact analysis and solution options.

5. Supporting Agile Ways of Working

• Break down engineering work into clear, testable backlog items that meet the Definition of Ready.
• Actively contribute to Agile ceremonies:
• Planning: provide feasibility, sizing, and technical constraints
• Refinement: structure engineering tasks, clarify implications
• Daily collaboration: support the squad in removing technical blockers
• Reviews: showcase engineering increments
• Retrospectives: drive process, quality, and tooling improvements
• Promote iterative delivery, early validation, and continuous learning to increase engineering maturity.

6. Change & Adoption

• Produce high‑quality technical documentation covering pipelines, data models, transformation logic, and operational behaviours.
• Support deployment, troubleshooting, and onboarding for new data products.
• Help ensure data products are understood, embedded in workflows, and delivering intended value across breweries and regions.

Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related technical field.
• Significant experience in data engineering within analytics‑driven or manufacturing/FMCG environments.
• Hands‑on experience delivering data platforms, pipelines, and models in Agile product teams.

Skills & Experience

Essential

• Strong software engineering skills in Python (OOP, packaging, testing frameworks).
• Excellent SQL proficiency and experience with large datasets.
• Handson expertise with AWS and Azure data engineering services (Lambda, Glue, Step Functions, S3, DynamoDB, etc.).
• Experience with Terraform and Infrastructure‑as‑Code practices.
• Familiarity with CI/CD, Git, and DevOps workflows.
• Understanding of ML lifecycle, including model deployment and monitoring.
• Ability to ingest, transform, validate, and structure diverse data sources.
• Strong communication skills and experience in global, cross‑functional product squads.
• Fluent English (spoken and written).

Desirable

• Experience with Spark or distributed data processing frameworks.
• Exposure to manufacturing, FMCG, or OT/IT data (MES, SCADA, historian, sensors).
• Experience with BI tools (Power BI, Grafana) and semantic modelling concepts.
• Knowledge of AWS analytics services (Athena, Redshift, SageMaker).

Mindset & Behaviours

Aligned with HEINEKEN Behaviours:

• Focus on value — ensuring engineering work drives measurable business outcomes.
• Empower teams — sharing knowledge openly and contributing to collective success.
• Start quick, learn fast — iterating, validating, and improving continuously.
• Continuously improve — raising engineering quality, performance, and maintainability.

You thrive in a squad‑based environment, enjoy solving complex data challenges, and play a key role in enabling smarter, more efficient, and data‑driven breweries.