Technical Delivery Manager

EXL; Pune District, Gurugram District, Bengaluru; Ibrido; INR 4.200.000 - 6.200.000; Tempo pieno

Descrizione del lavoro

EXL is seeking a highly experienced Technical Delivery Lead in Data Engineering & Data Platforms to drive enterprise data initiatives across Pune, Gurgaon, and Bangalore. The role demands 12+ years in data engineering and 4+ years in technical leadership, with strong leadership, client engagement, and governance skills.

The candidate will lead multi-cloud data architectures, enable AI-enabled data solutions, and mentor engineering teams, ensuring scalable, secure, and cost-efficient data

Competenze

• 12+ years of overall experience in Data Engineering, Data Platforms, Cloud Technologies, AI-enabled data solutions, and enterprise architecture.
• 4+ years of experience in a Technical Leadership role, including team management, mentoring, delivery ownership, and stakeholder collaboration.

Mansioni

• Provide technical leadership across Data Engineering, Cloud, AI, and enterprise data platform initiatives.
• Lead, manage, mentor, and guide engineering teams to ensure high-quality delivery and continuous capability development.
• Drive effective client engagement by understanding business priorities, managing expectations, communicating technical solutions clearly, and ensuring timely resolution of concerns.
• Collaborate with cross-functional teams including business stakeholders, architects, product owners, engineering teams, governance teams, and delivery leadership.
• Design and review scalable, secure, and cost-effective data architectures, data models, lakehouse platforms, and cloud-based data solutions.
• Support data cataloging, metadata management, data governance, data quality, and compliance practices across enterprise data platforms.
• Evaluate and implement AI/ML-enabled features, automation opportunities, and intelligent data engineering practices to improve platform efficiency and business outcomes.
• Drive best practices in Data Engineering, Cloud, Data Architecture, DevOps, CI/CD, performance optimization, and engineering excellence.
• Participate in technical assessments, solution reviews, interviews, and capability-building initiatives for Data Engineering roles.

Conoscenze

Databricks

Snowflake

AWS

Azure

GCP

Spark

PySpark

Python

SQL

Data modeling

Data governance

Data cataloging

Data warehousing

Lakehouse

Team leadership

Strumenti

Databricks

Snowflake

AWS

Azure

GCP

Descrizione del lavoro

Title: Technical Delivery Lead Data Engineering & Data Platforms

Experience: 12-15 years

Location: Pune / Gurgaon / Bangalore

Notice Period - Immediate to 30 days

Domain- Banking and Capital Markets, or Financial Services domain

Role Summary:

We are looking for a highly experienced Technical Lead with strong expertise in Data Engineering, AI-enabled data solutions, cloud platforms, data modeling, and data cataloging. The role requires strong technical leadership, team management, and client engagement skills to support enterprise data transformation initiatives. The individual will work closely with clients, leadership, architects, and cross-functional teams to drive solution design, delivery excellence, technology governance, and engineering best practices.

Experience:

12+ years of overall experience in Data Engineering, Data Platforms, Cloud Technologies, AI-enabled data solutions, and enterprise architecture. 4+ years of experience in a Technical Leadership role, including team management, mentoring, delivery ownership, and stakeholder collaboration.

Key Responsibilities:

• Provide technical leadership across Data Engineering, Cloud, AI, and enterprise data platform initiatives.
• Lead, manage, mentor, and guide engineering teams to ensure high-quality delivery and continuous capability development.
• Drive effective client engagement by understanding business priorities, managing expectations, communicating technical solutions clearly, and ensuring timely resolution of concerns.
• Collaborate with cross-functional teams including business stakeholders, architects, product owners, engineering teams, governance teams, and delivery leadership.
• Design and review scalable, secure, and cost-effective data architectures, data models, lakehouse platforms, and cloud-based data solutions.
• Support data cataloging, metadata management, data governance, data quality, and compliance practices across enterprise data platforms.
• Evaluate and implement AI/ML-enabled features, automation opportunities, and intelligent data engineering practices to improve platform efficiency and business outcomes.
• Drive best practices in Data Engineering, Cloud, Data Architecture, DevOps, CI/CD, performance optimization, and engineering excellence.
• Participate in technical assessments, solution reviews, interviews, and capability-building initiatives for Data Engineering roles.

Required Skills

• Strong hands‑on expertise in Databricks and Snowflake. Strong experience with AWS, Azure, or GCP cloud platforms.
• Strong knowledge of Spark, PySpark, Python, SQL, and large‑scale data processing frameworks.
• Experience in data modeling, data cataloging, metadata management, data governance, and enterprise data management practices.
• Experience building Data Lakes, Data Warehouses, Lakehouse platforms, ETL/ELT pipelines, and orchestration workflows.
• Understanding of AI/ML concepts, GenAI‑enabled data solutions, automation use cases, and AI‑driven engineering practices.
• Strong team management, stakeholder management, client engagement, communication, and problem‑solving skills.

Preferred Skills:

• Experience in Banking and Capital Markets, or Financial Services domain.
• Exposure to data security, regulatory compliance, risk controls, and enterprise governance frameworks.
• Experience supporting hiring, technical evaluations, competency development, and engineering capability improvement initiatives.

Key Attributes:

• Strong technical depth across modern data, cloud, AI, and enterprise architecture ecosystems.
• Proven leadership experience in managing teams, driving delivery excellence, and building high‑performing engineering capabilities.
• Excellent client‑facing communication skills with the ability to manage expectations professionally and build trusted relationships.
• Ability to work effectively with cross‑functional teams and influence stakeholders across technical and business groups.
• Strategic thinker with a hands‑on technical mindset and strong ownership of outcomes.

Responsibilities:

• Key Responsibilities Provide technical leadership across Data Engineering, Cloud, AI, and enterprise data platform initiatives.
• Lead, manage, mentor, and guide engineering teams to ensure high‑quality delivery and continuous capability development.
• Drive effective client engagement by understanding business priorities, managing expectations, communicating technical solutions clearly, and ensuring timely resolution of concerns. Collaborate with cross‑functional teams including business stakeholders, architects, product owners, engineering teams, governance teams, and delivery leadership.
• Design and review scalable, secure, and cost‑effective data architectures, data models, lakehouse platforms, and cloud‑based data solutions.
• Support data cataloging, metadata management, data governance, data quality, and compliance practices across enterprise data platforms.
• Evaluate and implement AI/ML‑enabled features, automation opportunities, and intelligent data engineering practices to improve platform efficiency and business outcomes.
• Drive best practices in Data Engineering, Cloud, Data Architecture, DevOps, CI/CD, performance optimization, and engineering excellence.
• Participate in technical assessments, solution reviews, interviews, and capability‑building initiatives for Data Engineering roles.

Qualifications:

• Experience 12+ years of overall experience in Data Engineering, Data Platforms, Cloud Technologies, AI‑enabled data solutions, and enterprise architecture.
• 4+ years of experience in a Technical Leadership role, including team management, mentoring, delivery ownership, and stakeholder collaboration.