Responsibilities
- Architect and implement robust, scalable data pipelines for both batch and streaming workloads, setting high technical standards for the engineering team
- Design and maintain cloud-native data warehouse and lakehouse environments using Databricks as the central platform
- Lead technical strategy for data ingestion, transformation, and delivery layers within the assigned domain
- Develop and manage real-time data streaming solutions with Confluent Kafka to support use cases like point-of-sale data, digital orders, customer behavior, and supply chain monitoring
- Guide data modeling, schema development, and performance tuning across relational databases, Delta Lake, and NoSQL systems
- Enforce engineering best practices including code quality, peer review processes, CI/CD pipelines, automated testing, documentation, and system observability
- Build and maintain high-quality, well-documented data assets that are reliable, easy to discover, and optimized for analytical and AI applications
- Create foundational data structures that support artificial intelligence initiatives, including curated datasets, real-time feeds, feature-ready tables, and governed business semantics
- Work closely with data science and machine learning teams to transition experimental models into scalable, production-grade pipelines
- Define formal data product specifications covering schema, update frequency, quality metrics, and semantic meaning to support self-service data access
- Establish consistent, enterprise-wide data definitions used across digital commerce, marketing, supply chain, and finance functions
- Assess and integrate new technologies while maintaining hands-on involvement to keep the team technically current
- Collaborate with business units including digital commerce, marketing, supply chain, finance, and enterprise systems to understand requirements and deliver scalable data solutions
- Act as the primary technical liaison for the data domain, managing requirement gathering, solution design, and end-to-end delivery
- Coordinate with data architecture, analytics, data science, and platform engineering teams to align on standards, governance, and shared data products
- Enable downstream data usage by improving data accessibility, discoverability, and actionability across the organization
- Engage with business partners to jointly develop data products that align engineering efforts with strategic business outcomes
- Mentor and develop a team of data engineers, fostering a culture of ownership, technical rigor, and continuous improvement
- Conduct technical design reviews, architecture workshops, and collaborative coding sessions to elevate team capabilities
- Support career growth through personalized development plans, leveling frameworks, and coaching to help engineers achieve their potential
- Manage project resourcing to balance innovation, technical debt reduction, and operational support needs
- Recruit and retain skilled engineering talent by leveraging technical credibility and leadership
- Contribute to shaping the long-term data engineering strategy and roadmap, presenting architectural decisions and business value to executive stakeholders
- Promote modern data engineering methodologies such as lakehouse design, DataOps, event-driven architectures, and data mesh concepts
- Champion innovation by identifying and applying generative AI, automation tools, and advanced technologies to improve development speed and system efficiency
