We are seeking a highly accomplished, hands-on SVP – Engineering & Delivery Lead (Data Services) to join our technology leadership team in Pune. In this critical engineering role, you will serve as the Platform Tech Lead and take end-to-end ownership of the technical delivery, architectural alignment, and global engineering execution for our strategic Data Services platforms.
This is a senior software engineering leadership and technical delivery role designed for a seasoned software/data engineer with a proven track record of managing engineering teams and technically steering agile workstreams. In this role, you will hold direct line management responsibility for distributed agile engineering pods, providing people leadership, talent development, and technical oversight.
You will be responsible for driving the technical execution of key initiatives, including modernizing our core data services, scaling our data lakehouse infrastructure, and engineering critical regulatory-driven technology programs, compliance commitments, and enterprise technology mandates. You will own team capacity planning, hiring, talent development, and performance evaluations.
A core expectation of this role is an AI-First Thinking Mindset. You will actively champion and drive the adoption of generative AI tools (such as GitHub Copilot, Claude, ChatGPT, and other developer productivity assistants) to significantly accelerate software delivery, automate testing, and improve code quality across your teams, adapting seamlessly to the enterprise-approved tools available within our network. You will ensure that all engineering deliverables strictly align with our target architecture, enforce rigorous code and design standards, and maintain hands-on technical oversight and architectural alignment across multiple global agile engineering workstreams.
1. Engineering Leadership & People Management
Engineering Line Management: Manage, hire, develop, and conduct performance evaluations for a distributed, multi-location team of software and data engineers, ensuring continuous talent growth and high team engagement.
Agile Pod Delivery: Provide hands-on technical leadership and functional management across agile engineering pods, driving daily sprint execution, code reviews, and architectural alignment.
AI-First Software Delivery: Champion and drive an AI-first developer culture. Promote the adoption of generative AI coding assistants and modern productivity tools to accelerate software development, automate unit test generation, and streamline code refactoring across your engineering teams.
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End-to-End Technical Delivery: Take full accountability for the technical delivery of data services, real-time pipelines, and platform integrations from inception to production deployment.
Engineering Excellence: Foster a culture of high-quality software engineering, enforcing rigorous unit testing, automated integration testing, and continuous integration/continuous delivery (CI/CD) practices.
2. Architectural Alignment & Technical Governance
Architectural Adherence: Ensure all engineering deliverables and system designs strictly align with the target enterprise architecture, design patterns, and future-state roadmaps defined by the Enterprise Architecture teams.
Technical Design & Review: Lead the technical implementation design of platform components, APIs, and data access patterns, ensuring they are optimized for high performance, low latency, and scalability.
Technical Debt Management: Proactively identify and remediate technical debt, ensuring the platform remains maintainable, resilient, and sustainable.
Security & Governance by Design: Enforce strict data security, row/column-level masking, and access control policies (e.g., Apache Ranger) within all engineered solutions.
Database Security Compliance: Ensure that all data storage and pipeline implementations adhere to rigorous enterprise security standards, including Transparent Data Encryption (TDE), secure connection protocols, and continuous database auditing.
3. Platform Modernization & API Engineering
Data Pipeline Engineering: Oversee the engineering of robust, high-throughput data pipelines across hybrid relational databases and modern cloud-native lakehouses.
API & Microservices Development: Lead the development of standardized, secure, and resilient microservices (primarily Java/Spring Boot) to support high-concurrency data consumption and AI-driven use cases.
4. Regulatory & Mandatory Engineering Commitments
Regulatory Engineering: Lead the technical execution of critical regulatory-driven technology programs, compliance commitments, and enterprise technology mandates, ensuring engineered solutions are robust, compliant, and delivered within strict timelines.
Audit-Ready Standards: Ensure all technical documentation, system designs, and deployment pipelines meet rigorous internal audit and regulatory compliance standards.
Required Technical Skillsets (Must be Hands-on)
Java Software Engineering (Core Competence): Advanced, hands-on expertise in Java and enterprise Java frameworks (specifically Spring Boot, Spring Framework, Hibernate/JPA). Proven track record of developing high-throughput, low-latency, and highly secure backend microservices, custom data connectors, and distributed processing modules.
Database & NoSQL Engineering (Must-Have): Deep, hands-on experience designing, developing, and tuning enterprise-scale relational databases (Oracle and SQL Server) and document-based NoSQL stores (MongoDB). Strong proficiency in writing and optimizing complex queries (advanced SQL), managing indexing strategies, and designing high-availability, clustered, and replicated database architectures (e.g., replica sets, multi-node clustering).
AI-First Developer Skills (Must-Have): Proven capability and hands-on experience using generative AI tools (e.g., GitHub Copilot, Claude, ChatGPT, or equivalent) to accelerate software delivery, write code, generate tests, and optimize development workflows.
API & Integration Engineering: Proven experience designing and building secure, high-performance REST/gRPC APIs, message queues (specifically Apache Kafka or TIBCO EMS), and secure data access patterns.
CI/CD & DevOps (Must-Have): Hands-on experience building, configuring, and maintaining deployment pipelines in Linux environments. Strong expertise with enterprise CI/CD platforms (specifically Harness or Jenkins), containerization/orchestration (Docker, Kubernetes, and RedHat OpenShift), and enterprise workload automation/job scheduling (Autosys).
Preferred / Nice-to-Have Skillsets
Data Virtualization & Federation: Technical and architectural understanding of query federation engines and data virtualization platforms, such as Starburst, Trino, Presto, Denodo, Dremio, AWS Athena, or Apache Drill.
Agentic AI & LLM Engineering: Conceptual or hands-on experience with Agentic AI frameworks and libraries (such as LangChain, LangGraph, Google ADK, or equivalent) for building autonomous AI agents, prompt engineering, and Retrieval-Augmented Generation (RAG) pipelines.
Distributed Data Processing: Familiarity or hands-on experience with distributed compute engines, specifically Apache Spark (PySpark, Spark SQL) or Apache Flink.
Modern Lakehouse Platforms: Experience working with cloud-native enterprise data lakehouse or warehouse platforms (e.g., Databricks, Snowflake, or equivalent).
Data Quality Automation: Familiarity with automated data quality frameworks (e.g., Great Expectations, dbt, Soda, or Deequ).
Programming Languages: Familiarity with Python or Scala for auxiliary data manipulation, scripting, or data science integrations.
Leadership & Methodology
Agile Engineering Delivery: Strong experience running engineering delivery within Scrum/Kanban frameworks, managing sprint closures, and leveraging enterprise tracking tools (specifically Jira and Confluence) to drive visibility and alignment.
Talent Management (Must-Have): Proven experience managing hiring, professional development, and performance reviews for technical teams across distributed or international offices.
Test Strategy Design: Proven track record of designing multi-layered testing strategies (unit, integration, regression, system, and regression parallel runs for migrations).
Experience & Qualifications
Total Engineering Experience: Minimum 12-15+ years of progressive experience in software engineering, data engineering, or platform development within a large-scale, highly regulated environment (preferably financial services).
Technical Leadership & Line Management: Minimum 5+ years of direct experience as an engineering line manager, principal engineer, or platform lead with direct people management responsibilities over software/data engineering teams, including experience leading teams across distributed or multi-location hubs.
Agile Delivery: Proven track record of driving technical delivery within Scrum/Kanban frameworks, managing sprint commitments, and collaborating closely with product owners.
Location Requirement: Based in or willing to relocate to Pune, India (hiring hub), with a proven capability of collaborating with, influencing, and aligning global engineering teams across multiple time zones
Education: Bachelor’s or Master’s degree in Computer Science, Information Systems, or a related engineering field.
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Most Relevant Skills
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Other Relevant Skills
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