Engagement Delivery Lead Data Analytics Gaming​/Casino Exp; Remote

Role Summary

The Associate Director – Data Engineering is a senior leadership role responsible for acting as the Delivery Leader across a portfolio of client accounts, ensuring consistent, high-quality program delivery through strong collaboration with offshore leads and teams. In parallel, this role owns the P&L for the account portfolio and drives proposal development and opportunity conversion, translating sales pipeline into executable, well-scoped delivery engagements.

This is a dual-mandate role: delivery excellence is the first and non-negotiable priority, while P&L ownership and business development (proposals/opportunity conversion) is the secondary priority, to be managed without compromising delivery commitments.

• Serve as the primary delivery owner and escalation point for all in-flight programs within the assigned account portfolio.
• Partner with offshore leads/managers to plan sprints/releases, allocate resources, and track execution against milestones.
• Drive daily/weekly delivery governance cadences (stand-ups, steering committees, status reviews) with onshore and offshore teams.
• Proactively identify delivery risks (technical debt, resourcing gaps, scope creep, attrition) and drive mitigation plans.
• Ensure adherence to engineering best practices, data quality standards, architecture guidelines, and Dev Ops/Data Ops processes across teams.
• Own client relationship management at the program level — building trust, managing expectations, and ensuring high CSAT (Client Satisfaction) scores.
• Conduct periodic program health checks and quality audits across accounts.
• Mentor and develop offshore leads and technical managers to strengthen delivery capability and reduce dependency on escalations.
• Ensure knowledge continuity and succession planning within delivery teams.
• Manage cross-functional dependencies (infrastructure, security, data governance, QA) required for successful delivery.

• Own and manage the P&L for the assigned set of accounts, including revenue recognition, cost management, and margin improvement.
• Track utilization, billability, and pyramid health of the delivery teams; drive corrective actions where needed.
• Forecast resourcing and financial requirements in line with account growth plans.
• Identify cost optimization opportunities (offshore-onshore mix, automation, tooling) without compromising delivery quality.
• Report account financial performance to leadership on a periodic basis.

• Partner with sales, account management, and pre-sales teams to identify and shape new opportunities within existing and prospective accounts.
• Lead or contribute to solutioning, effort estimation, staffing plans, and commercial proposals for new deals or account expansions (RFP/RFI responses, SOWs).
• Ensure proposed solutions are technically sound, deliverable, and aligned with realistic timelines and offshore delivery capability.
• Support due diligence, client presentations, and negotiations during the opportunity-to-contract transition.
• Ensure a smooth handover from the proposal/sales stage to the delivery team, including transfer of context, assumptions, and commitments made.

• Act as the bridge between onshore leadership/clients and offshore delivery teams, ensuring alignment of priorities and expectations.
• Build and maintain strong working relationships with client stakeholders, sponsors, and internal cross-functional leaders.
• Drive a culture of ownership, accountability, and continuous improvement within offshore and onshore teams.
• Support hiring, onboarding, and capability-building initiatives for the account's delivery teams.

• 16+ years of overall experience in Data Engineering / Data Platform delivery / AIML, Databricks with at least 3–5 years in a delivery leadership or account management role.
• Proven experience managing multi-account or multi-program portfolios with distributed (onshore-offshore) delivery models.
• Strong technical grounding in data engineering concepts — data pipelines, ETL/ELT, data warehousing, cloud data platforms (AWS/Azure/GCP), big data…