Job Title
People Data Engineer
Job Description
Job Description Summary
Build reliable, secure and scalable data products and data-lake capabilities that power AI-enabled applications, automations and analytics experiences across Philips. You will use modern data-engineering practices, Python, SQL and Databricks to make trusted data accessible to the People Analytics Development Pod and its business stakeholders.
In this role, you have the opportunity to
Help shape the data foundations behind People Analytics products that enable better, evidence-based decisions across Philips.
As part of the People Analytics Development Pod, you will help establish and operate a governed data lake, alongside the pipelines, curated datasets and integrations that support analytical models, AI and LLM capabilities, automations and full-stack applications. You will work closely with Data Scientists and Full Stack AI Application Engineers to turn business needs into trusted, reusable data products.
You are responsible for
•Helping to design, set up and operate a secure, governed data lake for People Analytics and approved Philips business use cases.
•Establishing practical data-lake foundations, including ingestion zones, curated layers, access controls, data-quality standards, metadata and documentation.
•Building, testing and maintaining robust data pipelines using Python, SQL, Databricks and approved enterprise tooling.
•Ingesting, transforming and preparing data from approved enterprise systems for analytics, AI and application use cases.
•Designing and maintaining curated, reusable datasets and data products for the People Analytics Development Pod.
•Implementing data-quality checks, reconciliation, monitoring and alerting to ensure reliable data delivery.
•Supporting the development of data models that are understandable, well documented and fit for analytics and product use.
•Developing secure APIs, extracts or data-access patterns that enable approved applications and automations to use trusted data.
•Working with Data Scientists to prepare reliable analytical datasets, feature sets and model inputs.
•Working with Full Stack AI Application Engineers to provide performant, governed data access for React and Node.js applications.
•Supporting AI and LLM-enabled products with high-quality source data, document preparation, metadata, retrieval-ready datasets and appropriate data-access controls.
•Using Databricks capabilities to develop, orchestrate and operationalise data workflows.
•Applying data privacy, security, retention, access-control and compliance requirements when working with sensitive employee or business data.
•Participating in code review, automated testing, CI/CD, documentation and incident-resolution practices.
•Investigating pipeline failures, data-quality issues and performance bottlenecks, and contributing to their resolution.
•Documenting data sources, transformations, lineage, quality rules and operational processes.
•Collaborating with business and technical stakeholders to understand data needs and translate them into maintainable engineering solutions.
You are a part of
The People Analytics Development Pod within the People Intelligence organization.
The pod builds high-value digital products, AI-enabled applications, automations and analytics experiences. The Data Engineer provides the trusted data foundation that enables the pod to build solutions for People Analytics and broader Philips business use cases.
You will work closely with:
•The People Analytics Lead and People Intelligence Analytics Partners
•Data Scientists
•Full Stack AI Application Engineers
•Product owners and business stakeholders across Philips
•Enterprise IT, cloud, architecture and security teams
•Privacy, compliance and responsible-AI teams
To succeed in this role, you will need
•A bachelor's degree in computer science, data engineering, information technology, software engineering, data science or a related field - or equivalent practical experience.
•At least 2 years of professional experience in data engineering, analytics engineering, software engineering or a related technical field.
•Strong hands-on SQL skills, including writing and optimising queries for analytical datasets.
•Practical Python experience for data processing, automation and pipeline development.
•Experience building, testing and maintaining data pipelines or transformation workflows.
•Familiarity with data-lake concepts, including ingestion, transformation, curated data layers, data governance and access management.
•Familiarity with Databricks or a similar modern cloud-data platform.
•Understanding of data modelling, data quality, data lineage and documentation practices.
•Experience working with APIs, files, databases and other data-integration patterns.
•Familiarity with version control, code review and automated testing.
•Basic understanding of CI/CD, deployment processes, monitoring and production support.
•Awareness of data privacy, access controls and secure handling of sensitive data.
•Strong problem-solving skills and attention to detail.
•The ability to communicate clearly with technical and non-technical stakeholders.
•A collaborative mindset and willingness to learn in a global, matrixed environment.
