Overview
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.
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.
- Sufficient depth in LangChain, LangGraph, vector databases, and knowledge graph platforms (Neo4j) to lead architecture reviews, evaluate technical trade-offs, and guide engineering decisions.
- Ability to assess AI system designs for production readiness: latency, cost, observability, reliability, and governance compliance.
Nice to Have
- Experience contributing to pre-sales: RFP responses, solution architecture proposals, effort estimation, and POC delivery.
- Published technical writing, conference talks, or documented program case studies.
Qualifications
- 10-12 years of experience in data and/or AI engineering, with the latter years in technical lead or project management roles.
- Demonstrated experience owning delivery of at least two large-scale, multi-workstream data or AI program from inception to production.
- Experience managing cross-functional engineering teams in a client-facing or consulting environment.
- Technical certifications in dbt, Snowflake, or a major cloud platform (Azure, AWS, GCP) are a strong plus.
