Senior Data Engineer(6+ years with MS Fabric, Azure, ADF, Data Platforms only)

Overview

Design, build, and operate modern data solutions that power data products, analytics, integrations, reporting, and AI capabilities. This is a hands-on engineering role combining data engineering fundamentals with modern cloud data platforms, APIs, integration patterns, and production-grade engineering practices.

What you'll do

• Design, develop, and maintain scalable data pipelines supporting operational, analytical, reporting, and AI use cases.
• Build batch, incremental, near-real-time, and event-driven data processing solutions.
• Develop data ingestion, transformation, orchestration, and delivery workflows using modern cloud data platforms.
• Design and implement data models optimized for performance, maintainability, and consumption.
• Develop reusable engineering components and patterns rather than one-off solutions.
• Optimize pipelines and data processing for reliability, scalability, performance, and cost.
• Contribute to technical design discussions and architecture decisions.
• Design and implement integrations across enterprise applications, data platforms, and third-party systems.
• Develop reusable integration services and data-access capabilities.
• Build secure REST and GraphQL APIs to expose data and platform capabilities.
• Implement event-driven and asynchronous integration patterns where appropriate.
• Apply appropriate authentication, authorization, versioning, monitoring, and API lifecycle practices.
• Build automated data validation, reconciliation, and quality controls into pipelines and data products.
• Implement appropriate unit, integration, and data testing as part of development.
• Build monitoring and observability into pipelines, APIs, and integration services.
• Troubleshoot complex production issues and perform root-cause analysis.
• Implement preventive improvements rather than repeatedly addressing symptoms.
• Use AI-assisted development throughout the engineering lifecycle, including design, coding, testing, documentation, troubleshooting, and optimization.
• Apply appropriate technical review and validation to AI-generated code and engineering artifacts.
• Practice spec-driven development by defining clear requirements, data contracts, API contracts, acceptance criteria, and test expectations before implementation.
• Contribute to the development of data solutions supporting AI agents, RAG, semantic models, machine learning, and other AI-enabled applications.
• Implement data quality, lineage, metadata, security, and auditability requirements within data solutions.
• Build data pipelines and integrations appropriate for sensitive and regulated data.
• Follow established governance, risk, security, and compliance requirements.
• Support secure and auditable processing of KYC, UCC, public-record, and other business data.
• Maintain clear technical documentation for data flows, APIs, transformations, and operational processes.
• Partner with architecture, security, governance, and platform teams to resolve technical and compliance requirements.
• Take ownership of solutions through development, deployment, and production operation.
• Monitor pipeline execution, API health, data quality, and platform performance.
• Investigate incidents and integration failures and drive issues through resolution.
• Participate in code reviews and continuously improve engineering practices.
• Automate deployment, testing, configuration, and operational processes wherever practical.
• Work closely with Lead Data Engineers, product teams, architects, analysts, AI teams, and business stakeholders.
• Translate requirements into practical and scalable technical solutions.
• Communicate technical decisions, dependencies, risks, and tradeoffs clearly.
• Share knowledge and reusable solutions across the engineering team.
• Support and mentor less-experienced engineers when appropriate.

What you'll need

• Bachelor's degree in Computer Science, Engineering, Information Systems, or a related discipline, or equivalent practical experience.
• 6+ years of experience in data engineering, integration engineering, software engineering, or related roles.
• Strong SQL skills with experience in data modeling, database design, and performance optimization.
• Hands-on experience designing and developing scalable ETL/ELT pipelines and data-processing solutions.
• Experience with one or more modern cloud data platforms, such as Databricks, Microsoft Fabric, Snowflake, or comparable technologies.
• Programming experience with Python, PySpark, Scala, Java, C#, or a comparable language.
• Experience developing and consuming REST APIs and working with modern API-based architectures.
• Experience with event-driven, messaging, or asynchronous integration patterns.
• Experience with cloud-native technologies across Azure, AWS, GCP, or comparable environments.
• Experience with source control, automated testing, CI/CD, monitoring, and modern DevOps practices.
• Understanding of data quality, governance, security, lineage, and cloud identity concepts.
• Experience supporting production data systems and troubleshooting complex technical issues.
• Practical experience using AI-assisted development tools in day-to-day engineering.
• Strong problem-solving, communication, and collaboration skills.

Nice to have

• Experience working across multiple modern data platforms or cloud technologies.
• Experience with Apache Spark, Delta Lake, Apache Iceberg, lakehouse architectures, or comparable technologies.
• Experience with GraphQL and enterprise API management platforms.
• Experience with streaming and messaging technologies such as Kafka, Event Hubs, Event Grid, Service Bus, or comparable technologies.
• Experience with serverless computing and cloud-native integration services.
• Experience with data products and data-as-a-product principles.
• Experience with modern data-sharing and interoperability patterns.
• Experience with Infrastructure as Code and deployment automation.
• Experience with AI-related data solutions such as RAG, semantic models, data agents, machine learning, or agent/tool integrations.
• Experience working with sensitive or highly governed data in financial services, legal, compliance, or regulatory environments.
• Familiarity with concepts such as KYC, UCC, public records, GRC, or SOC 2.

Details

• Location: Chennai, India (Neville Towers).
• Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process.
• Interviews must be conducted without AI tools or external prompts; virtual backgrounds must be removed and in-person interviews are included in the process.