At Quest Global, it’s not just what we do but how and why we do it that makes us different. With over 25 years as an engineering services provider, we believe in the power of doing things differently to make the impossible possible. Our people are driven by the desire to make the world a better place—to make a positive difference that contributes to a brighter future. We bring together technologies and industries, alongside the contributions of diverse individuals who are empowered by an intentional workplace culture, to solve problems better and faster.
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.
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.
Strong working knowledge of dbt - modelling conventions, incremental strategies, testing frameworks, documentation standards, and team-wide adoption at scale.
Experience designing and governing orchestration workflows in Apache Airflow - DAG design, SLA enforcement, retry strategies, and dependency management across production programs.
Proficiency with distributed processing: PySpark on Databricks, Delta Lake, Unity Catalog, and equivalent cloud-native data processing services.
Hands-on experience with cloud data warehouses - Snowflake, BigQuery, or Redshift - including performance tuning, RBAC, data contracts, partitioning, and governance.
Working knowledge of streaming platforms (Kafka) and event-driven pipeline patterns.
Strong SQL skills - query planning, window functions, CTEs, and performance tuning on large datasets.
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
PMP, CSM, or SAFe certification.
dbt Analytics Engineering Certification or cloud data certifications (GCP Professional Data Engineer, Azure DP-203, AWS Data Analytics Specialty).
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
Bachelor's or Master’s degree in computer science, Data Engineering, Information Systems, or a related technical discipline.
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.
PMP, CSM, or equivalent delivery management certification is preferred.
Technical certifications in dbt, Snowflake, or a major cloud platform (Azure, AWS, GCP) are a strong plus.
Strong portfolio of technical and delivery work - case studies, architecture artefacts, or team outcomes - is weighed heavily alongside formal credentials.
We are known for our extraordinary people who make the impossible possible every day. Questians are driven by hunger, humility, and aspiration. We believe that our company culture is the key to our ability to make a true difference in every industry we reach. Our teams regularly invest time and dedicated effort into internal culture work, ensuring that all voices are heard.
We wholeheartedly believe in the diversity of thought that comes with fostering a culture rooted in respect, where everyone belongs, is valued, and feels inspired to share their ideas. We know embracing our unique differences makes us better, and that solving the worlds hardest engineering problems requires diverse ideas, perspectives, and backgrounds. We shine the brightest when we tap into the many dimensions that thrive across over 21,000 difference-makers in our workplace.
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