Head Data Engineering

Location : Chennai / Hyderabad

Domain: Wealth Management / Asset Management

Role Overview

We are seeking an experienced Head of Data Engineering / Data Practice Head to build and lead the data engineering practice for Wealth Management and Asset Management platforms. The role will own the data strategy, engineering capabilities, architecture, delivery standards, and modernization of enterprise data platforms supporting investment, portfolio, client, and regulatory use cases.

The ideal candidate will combine strong data engineering leadership and technology expertise with deep understanding of Wealth/Asset Management data, investment processes, and financial products.

Key Responsibilities

Data Practice Leadership

• Build and lead a high-performing Data Engineering practice covering architecture, engineering, platform, governance, and delivery.
• Define the data engineering strategy, technology roadmap, standards, and best practices aligned to business objectives.
• Build engineering capabilities, talent strategy, operating models, and delivery practices across the data organization.

Data Engineering & Platform

• Lead the design and modernization of scalable enterprise data platforms, data lakes/lakehouses, warehouses, and real-time data pipelines.
• Drive adoption of modern cloud data technologies, distributed processing, APIs, streaming, and data integration frameworks.
• Establish standards for data quality, lineage, observability, security, scalability, and performance.
• Partner with Architecture, Product, Engineering, and Business teams to develop reusable data capabilities and platforms.

Wealth & Asset Management Domain

• Bring strong understanding of investment management, portfolio management, wealth management, asset management, securities, performance, risk, and client data.
• Drive data solutions across areas such as portfolio analytics, investment reporting, client insights, performance measurement, risk, and regulatory reporting.
• Understand data across the investment lifecycle, including orders, trades, positions, holdings, pricing, corporate actions, benchmarks, and performance.

Data & AI Enablement

• Build data foundations that enable advanced analytics, AI/ML, GenAI, and intelligent automation use cases.
• Partner with Data Science, AI, and Product teams to make high-quality, governed data readily accessible for analytics and AI applications.
• Establish practices for responsible and secure use of enterprise data.

Governance & Stakeholder Management

• Establish strong data governance, quality, security, and compliance practices in partnership with relevant functions.
• Work closely with senior business, technology, product, and client stakeholders to define priorities and deliver measurable outcomes.
• Drive technology modernization, continuous improvement, and engineering excellence across the data practice.

Qualifications & Experience

• 20+ years of experience in Data Engineering, Data Platforms, Data Architecture, or Technology, with significant leadership experience.
• Strong experience building and leading enterprise-scale Data Engineering practices and large engineering teams.
• Deep domain experience in Wealth Management, Asset Management, Investment Management, Capital Markets, or related financial services.
• Strong understanding of investment and wealth data, including portfolio, holdings, transactions, pricing, performance, risk, and client data.
• Proven experience with modern cloud data platforms, data lakes/lakehouses, data warehouses, ETL/ELT, streaming, APIs, and distributed data processing.
• Experience with cloud platforms such as AWS, Azure, or GCP and modern data engineering ecosystems.
• Strong understanding of data governance, quality, lineage, security, and regulatory requirements within financial services.
• Experience enabling AI/ML and advanced analytics through scalable data platforms.
• Strong executive stakeholder management and experience working with global financial institutions.
• Proven ability to build teams, define technology strategy, and scale a data engineering organization.