Director/AVP || Data Strategy & Engineering

Role Title: Head of Data Strategy & Engineering (Hands-On Leader)

Purpose of the Role

own and evolve the enterprise data ecosystem

align data engineering, governance, and analytics under a unified vision

1. Strategic Data Leadership

• Define and drive a

  cohesive data strategy

   that connects engineering, reporting, and business outcomes.
• Create a clear

  data architecture roadmap

   to scale the existing lake and integrate new sources seamlessly.
• Translate business objectives into

  data priorities and actionable roadmaps

  .
• Establish KPIs and frameworks to measure the value generated from data.

2. Platform & Architecture Evolution

• Take ownership of the existing

  data lake

   and elevate it into a robust, governed, AI-ready environment.
• Oversee and guide data engineers in improving data models, pipeline reliability, and scalability.
• Introduce

  best practices

   in orchestration, metadata management, lineage tracking, and performance optimization.
• Lead modernization initiatives where needed (e.g., real-time data streams, feature store, API exposure).

3. Data Governance & Quality

• Define and implement a

  comprehensive governance framework

   — including security, access controls, compliance, lineage, and data cataloging.
• Drive initiatives to improve data quality, standardization, and documentation.
• Establish clear ownership models for data assets across the enterprise.

4. Analytics & Business Enablement

• Partner closely with the

  existing Power BI/reporting team

   to move from operational dashboards to

  insight generation and predictive analytics

  .
• Standardize metric definitions, ensuring consistency across reporting layers.
• Build a

  semantic layer or KPI framework

   to speed up reporting and AI/ML enablement.

5. Team Leadership & Growth

• Provide

  technical leadership and mentorship

   to existing data engineers and reporting teams.
• Build a

  unified, high-performance team

   with clear roles, responsibilities, and accountability.
• Establish agile processes, sprint discipline, and a product mindset for data initiatives.
• Represent the data function in executive conversations and client-facing contexts.

6. AI Readiness & Innovation

• Work closely with Innovation and Product teams to:
• Prepare data for advanced ML use cases.
• Implement data contracts and feature stores.
• Enable conversational and predictive analytics.
• Influence

  build vs. buy

   decisions and overall architecture direction for AI enablement.

Core Competencies

• 10+ years in data engineering, architecture, or platform leadership.
• Hands-on experience with:
• Cloud data ecosystems (e.g., AWS, Snowflake, Databricks)
• Data pipeline orchestration & modeling
• Data governance and quality frameworks
• Business intelligence strategy (experience guiding Power BI or equivalent teams)
• Deep understanding of

  enterprise-scale data operations

   and system integration.

Strategic & Leadership Strength

• Proven ability to

  align fragmented data functions into a cohesive strategy

  .
• Strong communication skills — can engage credibly with executives, product, and operational leaders.
• Comfortable being

  both strategic and hands-on

   (not just directing others).
• Experience mentoring teams and building engineering maturity.

Bonus Points

• Prior experience in BPO, contact center, or service industry data environments.
• Exposure to ML pipelines and operationalization.
• Familiarity with conversational analytics or call center data.