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.
Preferred experience
- Experience helping to establish or operate a data lake, lakehouse or governed data platform.
- Experience with Databricks, including Spark, Delta Lake, workflows, SQL warehouses, Unity Catalog or equivalent capabilities.
- Experience with data-transformation tools and practices, such as dbt or equivalent.
- Experience building data products for analytics, machine learning, AI or web applications.
- Familiarity with REST APIs, Node.js services or Python-based APIs.
- Experience supporting retrieval-augmented generation, semantic search, document processing or AI-enabled applications.
- Experience with Azure services or another enterprise cloud environment.
- Experience with workflow orchestration, CI/CD, Docker or container-based deployment practices.
- Experience working with HR, workforce, talent or other sensitive and regulated data.
- Experience working with enterprise security, privacy and compliance teams.
- Experience working in global, matrixed organisations.
Key capabilities
Data-lake and lakehouse foundations; data engineering; Python and SQL; Databricks and modern data platforms; data pipelines and orchestration; data modelling and transformation; data quality and observability; APIs and enterprise integration; AI- and analytics-ready data products; secure data engineering; CI/CD and deployment fundamentals; documentation and data lineage; technical ownership and collaboration.
How we work together
We believe that collaboration and effective ways of working are essential to building meaningful solutions. For this role, we prefer candidates who can work from Bangalore and collaborate closely with local and global stakeholders.
About Philips
We are a health technology company. We built our entire company around the belief that every human matters, and we will not stop until everybody everywhere has access to the quality healthcare that we all deserve.