Job description
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.