Lead Data Engineer, Analytics Engineering

Role: Lead Data Engineer, Analytics Engineering

Location: Vashi, Navi Mumbai (4 days working from office)

Work Timings: Office work from 12:00pm to 5:00pm and then remote 7:30pm to 10:00pm

As a member of the Enterprise Data Platform team and Enterprise Technology organization within Technology & Engineering at PitchBook, you will be part of a team of big thinkers, innovators, and problem solvers who strive to deepen the positive impact we have on our customers and our company every day. We value curiosity and drive to find better ways of doing things. We thrive on customer empathy, which remains our focus when creating excellent customer experiences through product innovation.

We know that greatness is achieved through collaboration and diverse points of view, so we work closely with partners around the globe. As a team, we assume positive intent in each other’s words and actions, value constructive discussions and foster a respectful working environment built on integrity, growth, and business value. We invest heavily in our people, who are eager to learn and constantly improve. Join our team and grow with us!

As a Lead Data Engineer on the Enterprise Data Platform team, you will be responsible to lead and mentor a small team for building data pipelines to ingest various source data from enterprise technologies and PitchBook Platform data, manipulating (cleanse, dedupe, normalize) data into well-constructed data models for data analysis, implementing business logic and standard calculations, governing (supporting, observing, documenting) the data, and making the data available to end consumers in the form of PitchBook data products built on top of our data warehouse/data lake (e.g. Snowflake).

You will work with a range of data and reporting technologies (for example Python, Docker, Snowflake, and modern orchestration and transformation frameworks) and build on a strong foundation of rigor, quantitative techniques and efficient processing. You will join other engineers and analytics professionals who develop data pipelines, data products and insights for internal stakeholders across Sales, Customer Success, Marketing, Research, Data Operations, Product, Finance and Administration.

A core part of this role is shaping an analytics environment that is built for AI agents as much as for people. Our analysts, executives and AI agents should all query the same governed, documented, well-described data and get the same answers. You will lead the team to make that real, with semantic layers, governed metrics, rich metadata, agent-safe access, and evaluation of agent output, and in using agents to build faster and with higher quality.

Your team will rely on you to set the technical bar, coordinate their work at the scrum level, and help them grow, so that deliverables are high quality and arrive at the pace the business expects. You will collaborate with internal and external stakeholders of different roles and technical backgrounds, many with limited familiarity with data engineering, and you will have real influence over analytics and operational thinking across enterprise technology and product engineering.

You will exhibit a growth mindset, be willing to solicit feedback, engage others with empathy, and help create a culture of belonging, teamwork, and purpose. If you love building data-centric solutions, strive for excellence every day, are adaptable and focused, and believe work should be fun, come join us!

Primary Job Responsibilities

• Mentor two to three engineers through code and design reviews, pairing, and regular feedback. Help each grow in data modeling, pipeline orchestration design, testing, stakeholder skills and effective use of AI agents
• Run scrum-level coordination: sprint planning, backlog refinement, daily priorities, estimation, and clear tracking of progress and blockers in Jira
• Collaborate directly with the EM and partner teams on requirements and technical direction
• Design and own data products by turning business questions into viable data assets. Identify and prioritize operational improvement opportunities, and measure business KPIs against objectives
• Architect agent-ready data products with rich catalog metadata and an owned semantic layer of governed metric definitions
• Design, build, document and maintain scalable ELT pipelines and data models that turn enterprise data into trusted, governed data products and reporting structures for generating insights
• Consistently evolve data processes and techniques following industry best practices and drive down technical debt that meaningfully limits velocity or reliability
• Own the quality and reliability of data products through automated testing, monitoring, alerting, and a no-silent-failure publishing policy• Support the vision and values of the company through role modeling and encouraging desired behaviors
• Partner with stakeholders to clarify requirements, freshness needs and definitions, explain trade-offs in plain language, and represent the team in cross-team planning while sharing status, risks and priorities with engineering leadership

Skills and Qualifications

• Bachelor's degree in a related field (Computer Science, Engineering, etc.)
• Experience leading or mentoring engineers, including coordinating work in an agile or scrum environment and delivering to business deadlines
• 7+ years of experience in data engineering roles, including creating and maintaining data pipelines, data modeling, and data architecture
• 5+ years of experience in advanced SQL, including expert-level skills in querying large datasets from multiple sources and developing automated reporting
• 5+ years of experience in Python, with skills for diverse components of data pipelines, including scripting, data manipulation, custom extract, transform and loads, and statistical/regression analysis
• Expertise in extract, transform, and load (ETL) and extract, load, transform (ELT) processes and pipelines, platforms (e.g. Airflow), and distributed messaging (e.g. Kafka)
• Experience with tools that capture and control data modeling change management (SQLMesh preferred)
• Proficient in data storage solutions, data warehousing, and cloud-based data platforms (e.g. Snowflake)
• Hands-on experience preparing data for AI and LLM consumption, or a strong track record of semantic modeling and governed metric definitions with a clear plan to extend them to AI agents
• Knowledge and applicable working experience establishing and ensuring data governance, data quality, and compliance standards• Exceptional problem-solving skills
• Excellent communication and collaboration skills with the ability to engage with non-technical stakeholders
• Experience working with enterprise technologies (CRM, ERP, Marketing Automation Platforms, Financial Systems, etc.) is a plus

Working Conditions       

The job conditions for this position are in a standard office setting. Employees in this position use PC and phone on an on-going basis throughout the day. Limited corporate travel may be required to remote offices or other business meetings and events.

Morningstar India is an equal opportunity employer.

Morningstar's hybrid work environment gives you the opportunity to collaborate in-person each week as we've found that we're at our best when we're purposely together on a regular basis. In most of our locations, our hybrid work model is four days in-office each week. A range of other benefits are also available to enhance flexibility as needs change. No matter where you are, you'll have tools and resources to engage meaningfully with your global colleagues.