Principal Data And Analytics Engineer

Compensation Pay Range: $ - $

The actual hourly rate will equal or exceed the required minimum wage applicable to the job location. Additional compensation includes annual, quarterly performance, or premiums may be paid in amounts ranging per hour in specific circumstances. Premiums may be based on schedule, facility, season, or specific work performed. Multiple premiums may apply if applicable criteria are met.

The Principal Data and Analytics Engineer holds comprehensive responsibility for the design, implementation, and continuous evolution of the organization’s enterprise-wide data infrastructure and analytics capabilities. This role provides overarching technical vision, establishing architectural standards, and driving the long-term data strategy to facilitate critical business outcomes. Operating with a high degree of autonomy, this role influences executive leadership on data innovation, provides thought leadership and mentorship across the entire data and analytics engineering discipline, and champions data engineering maturity, innovation, scalability, security, and governance for all data assets.

They are instrumental in translating the most complex and ambiguous business challenges into innovative, high-impact data solutions that fundamentally shape the organization’s future.

Help define and evolve enterprise data engineering blueprints, including data mesh, medallion architecture, and hybrid cloud data platforms.

Set strategic direction for data platforms, tools, and services (e.g., Snowflake, GCP Big Query, dbt, Kafka, Airflow/Prefect) in alignment with future-state architecture and business priorities.

Architect and design highly scalable, resilient, cost optimal and secure data platforms.

Lead the design and implementation of next-generation data platforms, ensuring fault tolerance, high availability, and optimal performance for petabyte-scale data.

Establish and enforce organization-wide best practices for data pipeline development, CI/CD for data workflows, automated deployment playbooks, and robust rollback strategies.

Lead technology evaluation and adoption, proactively researching, evaluating, and championing the integration of cutting-edge data technologies, frameworks, and methodologies.

Define and scale enterprise knowledge management frameworks that ensure consistent documentation, discoverability, and reusability of data assets across domains.

Establish and govern standards for metadata management, data lineage, architectural diagrams, and runbooks.

Lead the design of federated governance models that empower domain-aligned teams to operate autonomously while conforming to centralized policies, frameworks and playbooks.

Collaborate with data governance, compliance, and security teams to operationalize policy-as-code frameworks for data retention, access control, and PII handling.

Advocate for and enable self-service knowledge discovery through tightly integrated cataloging tools (e.g., Alation, Collibra) and automated documentation generators.

Ensure robust documentation and versioning standards are embedded in CI/CD workflows for pipeline code, transformation logic, and schema changes.

Architect implementation of scalable, automated data quality frameworks that evaluate data at rest and in motion spanning completeness, timeliness, consistency, accuracy, and integrity.

Lead integration of data quality rules, metrics, and health indicators directly into orchestration layers (e.g., Prefect, Airflow) and transformation frameworks (e.g., dbt).

Evangelize a culture of data trust and transparency by integrating data quality insights into user-facing dashboards, alerts, and product health reports.

Identify and promote enterprise-wide data opportunities through thought leadership, white papers, reference architectures, and innovation labs.

Act as technical advisor to senior executives on data modernization, AI readiness, and platform consolidation strategies.

Serve as a strategic translator between complex business challenges and modern data architecture by leading domain-level and cross-domain data product strategy engagements.

Lead the design of enterprise-grade data products that align with OKRs, business transformation goals, and operational needs ensuring value realization across functional areas like supply chain, marketing, store ops, or customer satisfaction.

Architect and operationalize a unified enterprise-wide semantic layer, metrics store, and business logic abstraction that powers dashboards, self-service analytics, and machine-readable APIs.

Lead initiatives to unify KPIs, standardize metric definitions, and streamline business logic through reusable models.

Design composable data assets and feature stores that enable real-time and offline access patterns for ML models, AI agents, and decision orchestration systems.

Lead readiness initiatives for integrating data systems with LLM-powered agents and copilots, ensuring robust grounding data, latency…