Vice president - Data Architecture -CHENNAI

Job Overview

We are seeking a Senior Data Engineer / Data Platform Architect to play a critical role in the delivery of our enterprise-wide Data Mesh and Hybrid Cloud architecture. This role combines hands-on engineering with architectural ownership across core platform components—including data pipelines, storage layers, metadata systems, and federated access patterns. The ideal candidate brings deep technical expertise, an architect’s mindset, and a proactive approach to building resilient, scalable, and cloud-native data platforms. You will work closely with the Data Mesh and Cloud Architect Lead to design, implement, and operationalize enterprise-grade data products and services across Controls Technology and the broader Functions Technology organization.

Key Responsibilities

Architecture & Design:

• Co-architect modular, reusable, and secure data platform components supporting Data Mesh principles.
• Design optimal data storage, processing, and retrieval strategies across hybrid cloud environments, with a focus on Apache Iceberg-based architectures.
• Integrate federated querying frameworks using Starburst and Stargate, ensuring high-performance access across data sources.

Advanced Pipeline Engineering:

• Build and optimize complex batch and streaming pipelines for diverse data domains, with an emphasis on performance, fault-tolerance, and scalability.
• Enable schema evolution and transactional consistency in Iceberg tables, leveraging catalog services and metadata stores (e.g., Hive MetaStore).

Platform Enablement:

• Drive the adoption and implementation of Apache Iceberg, Iceberg Catalogs, Hive MetaStore, and Starburst to unify analytics across cloud and on-prem data assets.
• Operationalize data product templates, ingestion frameworks, and reusable patterns for use across multiple data domains.

Hybrid Cloud Integration:

• Architect and deploy data ingestion and processing components that span AWS cloud (S3, Lambda, Glue, Redshift, Athena) and on-prem environments.
• Implement data movement, governance, and monitoring frameworks that operate transparently across hybrid infrastructure.

Governance, Quality, and Security:

• Embed data quality, lineage, and metadata capture into the platform by default, ensuring compliance and traceability.
• Enforce role-based access controls, encryption standards, and audit mechanisms aligned with corporate policies.

Mentorship & Best Practices:

• Guide junior engineers and developers through technical reviews, architecture guidance, and solution design.
• Establish coding standards, testing patterns, CI/CD pipelines, and promote a culture of engineering excellence.

Required Technical Skills

Languages & Frameworks:

• Expert in Python, SQL, and scripting for automation and data engineering.
• Familiarity with Scala or Java is a plus.

Big Data & Processing:

• Strong experience with Apache Spark, Kafka, Flink, or other distributed data processing engines.
• Advanced knowledge of Apache Iceberg, including partitioning strategies, schema evolution, compaction, and ACID support.

Cloud & Hybrid Architecture:

• Hands-on with AWS cloud services—S3, Glue, Lambda, Redshift, Athena, and EMR.
• Experience architecting solutions across hybrid environments, integrating cloud-native and legacy systems.

Metadata & Federation:

• Proficient with Hive MetaStore, Iceberg Catalogs, and metadata management.
• Strong implementation experience with Starburst and Stargate for federated SQL access across disparate systems.

Infrastructure & DevOps:

• Working knowledge of Terraform, CloudFormation, Docker, GitHub Actions, or similar CI/CD and IaC tools.

Governance & Compliance:

• Familiarity with data lineage, cataloging, RBAC, encryption, and compliance standards (GDPR, CCPA, etc.).

Required Soft Skills

• Architectural Thinking – Ability to break down complex data platform needs into modular, scalable components.
• Leadership & Mentorship – Demonstrated experience in guiding junior team members, setting technical direction, and reviewing solutions.
• Business Orientation – Understands data’s role in business processes and can balance technical rigor with practical implementation.
• Collaboration – Comfortable working across multiple teams, geographies, and functions, including architects, engineers, product owners, and business users.
• Problem Solving – Strong analytical skills and a solution-oriented mindset for complex data challenges.

Qualifications

• 13+ years of experience in data engineering, big data platforms, and cloud-based infrastructure.
• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related technical discipline.
• Proven track record in building production-grade data platforms using Iceberg, Spark, and AWS services.
• Deep understanding of federated architectures, hybrid integration patterns, and metadata-driven pipelines.

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