Architect and evolve enterprise-scale data pipelines and platforms to support batch, real-time, and event-driven use cases.
Collaborate with stakeholders to gather requirements, frame technical tradeoffs, and shape data solutions that align with business priorities and long-term architectural direction.
Lead design and development of tabular and dimensional models across multiple business domains.
Build and optimize large-scale distributed data processing jobs with a focus on scalability, fault-tolerance, and cost-efficiency.
Establish and enforce best practices for data quality, governance, and observability across pipelines and systems.
Drive root-cause analysis for complex, systemic data issues and lead efforts to prevent recurrence.
Lead and oversee code reviews with a focus on scalability, maintainability, and enterprise-grade quality.
Influence and align data architecture strategy across hybrid environments (on-premises, multi-cloud, and SaaS).
Define and implement standards for CI/CD, security, monitoring, and incident management for data systems.
Lead adoption of modern data engineering practices and tooling, including workflow orchestration, automated testing, CI/CD, data quality controls, and observability.
Anticipate upstream and downstream impacts and design solutions to minimize risk and breakages in interconnected pipelines.
Monitor and improve performance of large-scale data environments, including cloud cost optimization.
Serve as technical escalation point for the most critical data incidents and participate in on-call rotations.
Provide on call support in standard rotation with other team members, to identify priority incidents
Mentor and coach engineers across teams, developing talent and building a culture of engineering excellence.
Provide technical leadership to distributed and offshore engineering teams through architecture guidance, code reviews, operational rigor, and clear communication across time zones.
Collaborate with leadership to identify and prioritize technical debt, architectural improvements, and process enhancements.
Evaluate and introduce new technologies by staying up to date on industry trends and assessing organizational fit.
Represent data engineering in cross-team forums, presenting strategy, challenges, and innovations to both technical and business stakeholders.