Technical Project Manager

Technical Project Manager - Data & AI Delivery Full-time · 10 years IT / 6 years end-to-end delivery · Hybrid, global client base What you'll own • Delivery, end to end. Data engineering, cloud and AI/ML engagements from kick-off to signed acceptance and handover - plan, schedule, RAID, status reporting and scope control - typically an 8–15-person multi-workstream POD, offshore/onsite hybrid, across time zones. • The commercials of delivery. Margin tracked weekly against the approved estimate; payment milestones and invoicing triggered on completion, not remembered at closure; rigorous change control - scope deltas flagged in writing the same day they surface, priced, and approved before a single hour of extra work begins. • Client-side dependencies. Access, environments, security clearances, decisions and sign-offs owned by the client - tracked with named owners, dated asks and what-if impacts, escalated on a clock (48 hours for blockers), never chased by mood. • The governance rhythm. Daily updates, weekly status reporting to client and internal leadership, steering committees - minutes, decisions and risks on record the same day. One version of the truth, reconciled to the tracker. • Honest reporting. Blocked capacity reported as blocked - with hours, reason and a recovery plan. A blocked project looks blocked on paper, never busy. Who you'll work with Engineering leads, solution architects and business analysts inside your POD; customer sponsors and IT/business SPOCs on the client side; and cloud/ISV partner teams - Microsoft, Databricks, AWS - including partner architects, partner-funded programme mechanics and partner reporting. Most Celebal engagements are three-sided: you manage all three sides. Must-have skills 1. 10 years of IT experience with a minimum of 6 years managing technology project delivery end to end - plan, schedule, RAID, status reporting, scope control. 2. Commercial ownership of fixed-bid and T&M delivery - margin protection, payment-milestone and invoicing discipline, and change control rigorous enough that no unapproved scope is ever in execution. 3. Hands-on project experience with data engineering and/or cloud platforms - Azure, AWS or GCP. Databricks / Lakehouse delivery exposure strongly preferred. 4. Technical fluency to discuss data pipelines, cloud infrastructure and integrations credibly with engineers - you don't write the code, but you can't be bluffed about it. 5. Experience coordinating AI/ML delivery and familiarity with the ML lifecycle (data prep, model development, deployment, monitoring), plus applied AI fluency in the PM craft itself - actively using AI tools (Copilot, ChatGPT/Claude or similar) for drafting plans and status narratives, summarising standups and stakeholder threads, and accelerating estimation and risk analysis - with sound judgment on client-data confidentiality when doing so. 6. A demonstrated system for managing client-side dependencies - named owners, dated asks, what-if impact statements, and disciplined escalation paths. In the interview, we will ask you to describe your mechanism, not your attitude. 7. Partner-ecosystem delivery experience - engagements delivered with or through Microsoft, Databricks, AWS or similar: co-delivery, partner-funded programmes, partner governance and evidence/claims reporting. 8. Strong command of Agile/Scrum and waterfall/hybrid stage-gate delivery, with judgment on when to use which - and the ability to run an agile sprint engine inside a commercial stage-gate wrapper. 9. Deep proficiency in at least two of Jira / Azure DevOps / MS Project / Smartsheet, plus reporting dashboards. 10. Strong estimation, risk management and progress-tracking ability, and executive-grade communication - comfortable presenting a red status to a steering committee with a recovery plan in the same breath. How you work • Structured thinker - cuts through ambiguity, asks the right questions, solves rather than escalates; escalates deliberately when a decision is above your level, with the history attached. • Curious and fast - picks up new technology and domains quickly and thrives as data and AI platforms evolve under your mid-engagement. • Bad news travels first, always with a proposed mitigation. Deviations are reported as facts with recovery plans - never as blame. • Documentation is a habit, not a chore: same-day minutes, decisions on record, signoffs with evidence. Good to have • SAP data ecosystem exposure - SAP data extraction/integration into modern platforms, including SAP-Databricks integration patterns. • Large-scale migration programme experience - Hadoop / Synapse / enterprise DWH to Lakehouse, Unity Catalog migrations, or equivalent platform-to-platform factories. • GenAI / LLM project delivery exposure (agents, RAG, model migration programmes). • Regulated-industry delivery - BFSI, telco, government: background-verification/NDA regimes, security-compliance gates (control-tracker driven), UAT/acceptance formality. • PMP, PMI-ACP, CSM, SAFe or PRINCE2 certification. • Cloud cost management / FinOps familiarity. What success looks like in your first 90 days • You run the weekly governance rhythm unaided - daily updates, weekly status report, steering committee - and your numbers reconcile to the trackers every time. • Zero unapproved scope in execution: every delta has a same-day flag and a CR decision on record. • Every client dependency has a named owner, a date and a what-if; nothing ages silently past five days. • Margin variance is explained in your weekly review - never discovered at an audit. • Your first stage gate or acceptance is passed with signed evidence archived. Celebal Technologies is a Databricks, Microsoft and AWS partner delivering data, AI and enterprise-platform solutions to global clients. This role reports into the Delivery organisation and operates the Cloud Delivery Management Framework - our five-phase, gate-governed delivery standard.