This role is for one of our clients Industry: IT Services and IT Consulting Seniority level: Mid-Senior level
Min Experience: 12+ years
Location: Bengaluru, Karnataka, India JobType: full-time
We are looking for a Technical Project Manager who can drive end-to-end execution of complex Data Engineering, Analytics, and AI/ML projects. The role requires managing engineering execution, coordinating cross-functional teams, handling stakeholder communication, and ensuring timely delivery of scalable data and AI solutions. The idea candidate should understand technical architecture well enough to work directly with engineers, architects, and data scientists while keeping delivery on track.
Requirements
Key Responsibilities
Project Delivery & Execution Management
- Own end-to-end delivery of complex technical projects from initiation to production deployment.
- Define project scope, timelines, milestones, deliverables, and execution strategy.
- Manage multiple concurrent technical projects with competing priorities.
- Ensure predictable delivery with strong governance around scope, timelines, quality, and execution.
- Drive project planning, sprint execution, release planning, and production readiness.
- Manage project risks, issue resolution, dependency tracking, and escalation management, financial metrics.
Team Leadership, Mentoring & People Management
- Proven experience managing and growing engineering teams of 10+ people - including hiring, performance management, and career development.
- Able to operate credibly in both technical design reviews and executive stakeholder meetings, switching registers fluently.
- Strong written and verbal communication: clear architecture decision records, concise board-level status updates, and structured client presentations.
- Conduct regular one-on-one discussions, coaching sessions, and career guidance.
- Drive team capability building and technical skill development initiatives.
- Foster accountability, ownership, collaboration, and engineering excellence culture.
- Support hiring, onboarding, and team expansion initiatives.
Technical: Data Engineering
- Deep, hands-on understanding of data engineering fundamentals: batch and streaming pipeline design, data modelling paradigms (dimensional, data vault, medallion architecture), and warehouse internals.
- Hands-on experience with cloud data warehouses – Databricks, Snowflake, BigQuery, AWS Redshift or Azure
- Experience with data quality frameworks, lineage tooling, and metadata cataloguing at an enterprise level.
Technical: AI & GenAI
- Practical experience designing or overseeing production AI systems - RAG pipelines, LLM integrations, vector search, knowledge graphs, or agentic frameworks.