The Role
Leads enterprise data strategy, architecture, and large-scale platform deployments across multi-cloud environments. Designs data warehouses, lakes, streaming pipelines, governance, testing, observability, and modern data stack integrations. Provides technical leadership and mentorship while partnering with business, analytics, data science, and product teams. Drives engineering standards, cost optimization, compliance, innovation, and adoption of technologies including Snowflake, DBT, Airflow, Kafka, Spark, and cloud platforms.
Summary Generated by Built In
Duties & Responsibilities
Define and Drive Enterprise Data Strategy
Develop and own the strategic architecture for enterprise-scale data platforms, pipelines, and ecosystems, ensuring alignment with business objectives and long-term scalability.
Architect High-Performance Data Solutions
Lead the design and implementation of robust, large-scale data solutions across multi-cloud environments (AWS, Azure, GCP), optimizing for performance, reliability, and cost efficiency.
Establish Engineering Excellence
Create and enforce best practices, coding standards, and architectural frameworks for data engineering teams, fostering a culture of quality, automation, and continuous improvement.
Provide Technical Leadership and Mentorship
Act as a trusted advisor and mentor to engineers across multiple teams and levels, guiding technical decisions, career development, and knowledge sharing.
Align Data Strategy with Business Goals
Partner with senior business and technology stakeholders to translate organizational objectives into actionable data strategies, ensuring measurable business impact.
Champion Modern Data Stack Adoption
Drive the adoption and integration of cutting-edge technologies such as Snowflake, DBT, Airflow, Kafka, Spark, and other orchestration and streaming tools to modernize data infrastructure.
Architect and Optimize Data Ecosystems
Design and manage enterprise-grade data warehouses, data lakes, and real-time streaming pipelines, ensuring scalability, security, and high availability.
Implement Enterprise Testing and Observability
Establish rigorous testing, validation, monitoring, and observability frameworks to guarantee data integrity, reliability, and compliance across all environments.
Ensure Governance and Compliance
Oversee data governance initiatives, including lineage tracking, security protocols, regulatory compliance, and privacy standards across platforms.
Enable Cross-Functional Collaboration
Work closely with analytics, data science, and product teams to deliver trusted, business-ready data that accelerates insights and decision-making.
Provide Thought Leadership
Stay ahead of emerging technologies and industry trends, influencing enterprise data strategy and advocating for innovative solutions that drive competitive advantage.
Lead Enterprise-Scale Deployments
Oversee large-scale data platform deployments, ensuring operational excellence, scalability, and cost optimization for global business needs.
Requirements
Basic Qualifications
Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related field (Master’s or equivalent advanced degree preferred)
11–15 years of progressive experience in data engineering or related fields
Deep expertise in SQL, data warehousing, and large-scale data modeling
Minimum 5 years of experience with Snowflake or another modern cloud data warehouse at scale
Minimum 3 years of experience with Python or equivalent programming languages for ETL/ELT and automation
Proven experience architecting data platforms on major cloud providers (AWS, Azure, or GCP)
Hands-on experience with orchestration tools (Airflow, ADF, Luigi, etc.) and data transformation frameworks (DBT)
Strong track record in designing and implementing large-scale data pipelines and solutions
Demonstrated experience leading cross-functional teams and mentoring senior engineers
Excellent communication skills for engaging with business stakeholders and cross-functional partners
Preferred Qualifications
Expertise in Real-Time Data Processing
Hands-on experience with streaming platforms such as Apache Kafka, Spark Streaming, and Apache Flink, enabling low-latency, high-throughput data pipelines for real-time analytics.
Strong DevOps and CI/CD Practices
Deep understanding of DevOps principles, automated CI/CD pipelines, and Git-based workflows tailored for data engineering environments, ensuring rapid, reliable deployments.
Domain Knowledge in Retail and E-Commerce
Proven experience working with customer-centric data ecosystems, leveraging data to drive personalization, operational efficiency, and business growth in retail or e-commerce contexts.
Track Record of Driving Innovation
Recognized for introducing technical innovations, improving engineering processes, and advancing organizational data maturity through strategic initiatives.
Experience in ML Ops and AI/ML Lifecycle
Practical knowledge of ML Ops frameworks, including building data pipelines for model training, managing feature stores, and implementing monitoring solutions for AI/ML models in production.
Strategic Alignment and Leadership
Ability to understand and interpret organizational vision and decision-making frameworks, aligning team objectives and personal goals to deliver measurable business impact.
Technology Evangelism and Trend Awareness
Up-to-date with emerging technologies, industry best practices, and modern data architectures; consistently brings innovative ideas and thought leadership to the team.
Skills Required
- Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field
- 11-15 years of progressive experience in data engineering or related fields
- Deep expertise in SQL, data warehousing, and large-scale data modeling
- At least 5 years of experience with Snowflake or another modern cloud data warehouse at scale
- At least 3 years of experience with Python or equivalent programming languages for ETL/ELT and automation
- Experience architecting data platforms on AWS, Azure, or GCP
- Hands-on experience with orchestration tools such as Airflow, ADF, or Luigi and data transformation frameworks such as DBT
- Experience designing and implementing large-scale data pipelines and solutions
- Experience leading cross-functional teams and mentoring senior engineers
- Excellent communication skills for business stakeholder and cross-functional engagement
- Master's degree or equivalent advanced degree
- Hands-on experience with Apache Kafka, Spark Streaming, or Apache Flink
- Strong DevOps, automated CI/CD, and Git-based workflow experience
- Retail or e-commerce domain knowledge
- Track record of driving technical innovation and improving engineering processes
- Experience with ML Ops, feature stores, model-training pipelines, and AI/ML production monitoring
- Ability to align team objectives with organizational strategy and measurable business impact
- Awareness of emerging technologies, industry best practices, and modern data architectures
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The Company
What We Do
Staples India is Staples’ technology and innovation hub in Chennai, building platforms, systems, and digital solutions that support the company’s global operations and future of work. Staples serves consumers and businesses with workplace products and services, including office supplies, janitorial products, technology, furniture, breakroom essentials, print and marketing, shipping, travel, and promotional offerings. Its India teams focus on engineering, eCommerce, process optimization, and enterprise solutions.
