As Director of Data Engineering – Data Center Assets, you will drive the strategy, design, and delivery of domain-oriented and cross-domain data products, focused around Data Center Assets and Operations. You will act as a technical and strategic leader, defining best-in-class data product architecture, ensuring scalability, reusability, and governance, and fostering a data product mindset across the enterprise.

Your mission is to build a unified, trusted, and holistic data ecosystem. You will design the blueprints that break down data silos, creating canonical data models and scalable integration patterns. By providing expert technical leadership, you will guide the development of platforms that unlock unprecedented insights, drive superior customer experiences, and foster operational excellence. You are the single point of technical ownership for the architecture and viability of these integrated data platforms.

Key Responsibilities:

Data Product Ownership & Strategy

• Lead the end-to-end lifecycle of data products: from vision, design, and development to deployment, monitoring, and continuous improvement.

• Define and maintain enterprise-level cross-domain data products mainly around Asset 260 and it’s linkage between Customer 360 and Product 360 by integrating data from multiple domains and systems (CRM, ERP, IoT, operational systems).

• Drive alignment between business priorities and data product design to enable self-service analytics and AI/ML initiatives

• Establish and evangelize data product standards (semantic models, metrics, API contracts) and ensure adoption across domains.

Solution Engineering

• Design modern, scalable, and secure data solutions leveraging cloud-native technologies (e.g., GCP, AWS or Azure).

• Ensure alignment with target enterprise architecture, platform capabilities (e.g., ingestion, transformation frameworks, orchestration, CI/CD, DataOps), and data governance.

• Collaborate with data scientists, data engineers, and business SMEs to design products that support AI/ML use cases, predictive models, and operational analytics.

Leadership & Collaboration

• Mentor and guide engineering teams in building reusable components, data models, and API

• Act as a key interface between platform engineering, domain engineering teams, product owners, and business stakeholders.

• Champion a data-as-a-product culture across the enterprise and promote platform enablement through data democratization.

Governance, Quality & Compliance

• Embed data quality, security, privacy, and compliance requirements (e.g., SOC2, GDPR) into data product design.

• Ensure traceability, lineage, and auditability of data assets and metrics from raw source to consumption.

Business & Stakeholder Alignment:

• Translate complex business requirements from sales, marketing, supply chain, and service into robust, scalable, and secure technical solutions.

• Collaborate closely with data governance councils and business data stewards to embed data quality and governance policies directly into the architectural design.

• Clearly articulate the technical vision, architectural decisions, and trade-offs to diverse audiences, from C-level executives to developers.

What You Bring:

• Passion for designing and delivering data solutions that enable trust, reuse, scalability, and agility across the enterprise.

• Ability to translate complex business and data concepts into actionable, high-quality solutions that drive business value.

• Proven track record of delivering enterprise-grade data products in large-scale environments (preferably in data center, cloud infrastructure, or similar domains).

• Strong sense of ownership, with a focus on building architectures and solutions that stand the test of time.

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or related field.

• 15+ years of experience in Data Engineering / Solution Architecture roles, with at least 5 years in senior/principal level engineering or architecture.

• Experience in leading cross-domain data products (Customer360, Product360, Asset360, etc.).

• Experience with data governance frameworks, metadata management, and enterprise data platforms.

• Knowledge of data privacy and security frameworks

Preferred Qualifications

• Experience delivering analytics self-service capabilities, conversational analytics and enterprise dashboards using BI tools (e.g., Looker, Power BI, Tableau).

• Familiarity with AI/ML operationalization, including enabling data pipelines for AI/ML model development and deployment.

• Knowledge of data mesh principles or federated data architectures.

• Experience with cost optimization and cloud resource management for data workloads.