Associate Director - Data & AI Engineering

Description:

Project Delivery & Execution:

- Ensure on-time, high-quality delivery of Data & AI solutions as per project requirements and business priorities.

- Monitor project progress, identify risks, and implement mitigation strategies.

- Optimize resource allocation and resolve delivery bottlenecks proactively.

Stakeholder Engagement:

- Work closely with US Senior Directors, Data & AI Managers, Tech Data Product Managers, and business stakeholders to understand goals and requirements.

- Communicate project status, risks, and achievements clearly and regularly to all stakeholders.

- Gather feedback and drive continuous improvement in delivery processes and outcomes.

Process & Quality Management:

- Establish and enforce best practices in Agile delivery, quality assurance, and documentation.

- Implement metrics to track efficiency, productivity, and quality across teams.

- Lead retrospectives and process improvement initiatives.

Collaboration & Communication:

- Facilitate effective communication between offshore and onshore teams.

- Coordinate cross-functional efforts to ensure alignment and synergy.

- Resolve conflicts and promote a positive work environment.

People Leadership

Team Leadership & Management:

- Direct and oversee multiple Data & AI delivery teams totaling around 40 Data & AI engineers and Pod Delivery Leads.

- Provide mentorship, guidance, and performance management to team members and leads.

- Foster a culture of collaboration, innovation, and accountability across offshore teams.

Direct/Indirect Reports

Basic Qualifications

- Bachelor's or master's degree in computer science, Engineering, Information Technology, or related field.

- 7+ years of experience in IT delivery management, with a focus on Data & AI projects.

- Proven experience managing multiple teams and delivery leads in an offshore/onshore model.

- Strong understanding of Data & AI technologies (Snowflake, Databricks, DBT), project management methodologies (Agile, Scrum), and best practices.

- Strong Knowledge of DevOps, CI/CD pipelines, Git, and its practices in a data engineering context.

- Exceptional communication, leadership, and stakeholder management skills.

- Ability to work across time zones and cultures, with a flexible and proactive approach.

- Experience collaborating with senior executives, technical product managers, and business stakeholders in the US preferred.

Preferred Qualifications

- Experience with real-time/streaming platforms (Kafka, Spark Streaming, Flink)

- Domain expertise in e-commerce, retail, or customer-centric data ecosystems

- Recognized for driving technical innovation, process improvement, or organizational data maturity

- Experience in ML Ops and AI/ML model lifecycle management including (data pipelines for training, feature stores, monitoring)

- Understand organization vision and decision-making framework. Able to align the team, personal goals/objectives contribute to the organization vision

- Understands the technology landscape, up to date on current technology trends and new technology, brings new ideas to the team