Lead Data Engineer (10+ years with MS Fabric, Azure, ADF, Data Platforms only)
Wolters Kluwer4 days ago
Chennai, IndiaStaff+
Responsibilities
- Provide hands-on technical leadership, set engineering standards, lead architecture and design reviews, and mentor data engineers.
- Architect and build scalable enterprise data platforms covering lakehouse, warehouse, data lake, streaming, data products, and cloud-native services.
- Develop reusable frameworks for ingestion, transformation, orchestration, data quality, metadata, data delivery, and data access.
- Build batch, incremental, near-real-time, and event-driven processing solutions with scalable data models.
- Champion AI-assisted engineering across requirements, architecture, development, testing, documentation, troubleshooting, and operations.
- Establish automated data quality, reconciliation, validation, testing, observability, reliability, and root-cause analysis practices.
- Design secure integrations and governed REST and GraphQL APIs using modern identity, authentication, and authorization patterns.
- Embed data governance, lineage, metadata, security, privacy, auditability, and compliance into platform designs.
- Partner with product, architecture, analytics, AI, operations, business, cybersecurity, governance, and compliance teams.
- Evaluate technologies and lead platform modernization, migration, and interoperability initiatives.
Requirements
- Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related discipline, or equivalent practical experience.
- 10+ years of experience in data engineering, software engineering, data platforms, or related disciplines.
- Experience providing technical leadership, mentoring engineers, and leading complex initiatives from architecture through production.
- Strong knowledge of modern data platform and distributed data architecture principles.
- Advanced SQL skills and strong expertise in data modeling, database design, and performance optimization.
- Deep experience designing scalable ETL/ELT pipelines and reusable data engineering frameworks.
- Significant experience with one or more modern cloud data platforms such as Databricks, Microsoft Fabric, Snowflake, or comparable platforms.
- Strong programming experience with Python, PySpark, Scala, Java, C#, or comparable languages.
- Experience with Azure, AWS, GCP, or comparable cloud environments; APIs; distributed processing; event-driven architectures; and modern integration patterns.
- Experience with automated testing, source control, observability, and modern DevOps practices.
- Understanding of data governance, lineage, security, identity, and data quality practices.
- Practical experience incorporating AI-assisted tools into engineering workflows.
- Strong communication, technical judgment, problem-solving, and stakeholder-influence skills.
- Preferred experience includes multi-platform or multi-cloud architecture, lakehouse technologies, Apache Spark, Delta Lake, Apache Iceberg, data products, governed APIs, AI-oriented data architectures, streaming and messaging, API management, cloud automation, regulated data, and platform modernization.
Tech Stack
Categories
Data Engineering
About Wolters Kluwer
Wolters Kluwer builds subscription software, data, and workflow solutions for professionals in healthcare, tax and accounting, legal and regulatory, finance and compliance, and ESG. Its portfolio spans SaaS applications and expert content that support compliance, decision-making, and operational processes. Founded in 1836 and headquartered in Alphen aan den Rijn, the Netherlands, it is publicly traded on Euronext Amsterdam (WKL) and reported 2025 annual revenue of €6.1 billion.