Technical Project Manager - Data Engineering & Analytics

Technical Project Manager - Data Engineering & Analytics will be responsible for driving multiple data and analytics client projects and implementations while acting as the controlling authority for assigned initiatives. Key responsibilities include developing solid technical and functional understanding of data platforms/offerings, project planning & execution, tracking & monitoring, reporting, communication to internal and client stakeholders, project closure, and client satisfaction.

• What you will do
• As a TPM for Data Engineering & Analytics, you will be responsible for the following:
• Develop a strong technical and functional understanding of assigned data engineering, analytics, and BI projects
• Develop and maintain project plans, timelines, and budgets aligned with data pipeline complexity and infrastructure requirements
• Lead cross-functional teams (data engineers, analysts, architects, infrastructure teams) to ensure project objectives are met and deliverables completed on time and under budget
• Track, monitor, and report on data project metrics including data quality, pipeline performance, and analytics adoption rates
• Implement metrics-driven project management Define and publish KPIs periodically (data ingestion rates, query performance, SLA compliance) and take corrective actions as necessary
• Communicate effectively with client and internal stakeholders to provide data project updates, technical feasibility assessments, and address data-specific concerns
• Provide mentorship and guidance to junior project managers specializing in data and analytics domains
• Collaborate with Data Architecture, Engineering, and Analytics teams to ensure successful project integration and alignment with organizational goals
• Manage data project funding/invoicing timely and accurately
• Any other activities assigned by the management team

What you will need to have

Experience Requirements:

Software Services Industry Experience: Minimum of 8 years in software development and services

Relevant Experience:

• Minimum of 5+ years of Technical Project Management
• Prior experience managing and delivering Data Engineering, Analytics, or BI projects (mandatory)
• Prior experience with cloud data platforms (AWS, Azure, GCP) or data warehouse solutions (Snowflake, BigQuery, Redshift)
• Prior software development experience in any stream
• Experience with Agile/Scrum methodologies in data projects is a plus

• Proven track record of delivering data projects on-time and under budget
• Proven experience in metrics-driven project management with focus on data quality and performance metrics
• Strong understanding of data engineering concepts (ETL/ELT, data pipelines, data modeling, data governance)
• Familiarity with analytics and BI tools (Tableau, Looker, Power BI, etc.)
• Team player with strong leadership and conflict management skills
• Excellent written and oral English communication skills
• Ability to work with multiple internal & client stakeholders, manage priorities, and negotiate effectively
• Ability to manage multiple data projects simultaneously
• Ability to work in a fast-paced, dynamic environment
• Strong problem-solving skills with ability to understand technical constraints and trade-offs

• Must be willing to work full time out of the Client Bangalore office for the first 3 months
• PMP or similar certification (PMP, PRINCE2, or Data-specific certifications like Google Cloud Data Engineer) is desirable
• Experience with data governance frameworks and compliance requirements (GDPR, data privacy) is a plus

Key Modifications Made:

Tailored for Data & Analytics:

Added emphasis on data pipeline complexity and infrastructure requirements

Included data-specific metrics (data quality, pipeline performance, SLA compliance)

Added cloud data platform experience as a requirement

Included analytics and BI tools familiarity

Added data governance and compliance knowledge

Emphasized cross-functional collaboration with data teams

Added Agile methodologies in data context