Principal Data And Analytics Engineer

O'Reilly Auto Parts

1d

About The Position

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.

Requirements

• Proven experience architecting enterprise-scale data platforms and ecosystems, including hybrid and cloud-native environments (e.g., GCP BigQuery, Snowflake, Iceberg, Advanced SQL, Erwin, dbt, Kafka, Alation, Collibra)
• Deep expertise in designing and scaling highly available, secure, and fault-tolerant batch and streaming pipelines with strong emphasis on cost optimization, observability, and latency control.
• Advanced proficiency in semantic modeling, reusable data asset design, and cross-functional data product delivery aligned to medallion architecture.
• Leadership in implementing CI/CD-enabled pipelines, RBAC frameworks, schema evolution strategies, and interoperable data exchange using Iceberg or equivalent table formats.
• Ownership of organization-wide metrics store and semantic layers, ensuring consistency, governance, and performance across reporting, AI, and ML use cases.
• Advanced expertise in programming languages such as Python, Scala, with the ability to architect complex data solutions.
• Demonstrated leadership in designing and overseeing the implementation of scalable, idempotent workflows using orchestration frameworks such as Airflow and Prefect.
• Demonstrated ability to translate business transformation goals into scalable data solutions and reusable patterns.
• Deep understanding of business processes, KPIs, and capability maps across functions such as supply chain, customer, store ops, and finance.
• Proven experience in driving cross-functional data product prioritization, influencing senior stakeholders, and quantifying impact of data initiatives.
• Experience shaping enterprise-wide data strategy by defining the long-term technical vision and architectural evolution roadmap across platforms, domains, and business units driving adoption of scalable, and governed data products.
• Experience leading platform modernization, tool evaluation, and architecture standardization across business domains.
• Expert competency in analytical and problem-solving that is crucial for identifying and resolving issues.
• Expertise in defining and enforcing enterprise-level data governance, metadata standards, and policy-as-code frameworks.
• Led the design and deployment of automated data quality management systems across ingestion, transformation, and consumption layers.
• Drive strategic KPI standardization by partnering with stakeholders, data stewards, and product teams to architect reusable semantic layers and metric definitions that enable trustworthy insights and LLM agent reasoning.

Nice To Haves

• Proven success enabling Iceberg-based, multi-cloud, interoperable data platforms with robust metadata, access control, and lineage frameworks.
• Experience integrating testing and validation frameworks into CI/CD workflows for dbt transformations, pipeline observability, and ML feature testing.
• Experience preparing enterprise data platforms for trustworthy insights, AI agentic readiness, including semantic alignment, real-time feature pipelines, and explain ability.
• Communication and presentation skills with the ability to convey complex architectural decisions to both technical and non-technical stakeholders.

Responsibilities

• 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 BigQuery, 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 optimization, and lineage tracking.
• Drive innovation in analytics automation, including anomaly detection, agent-triggered insights,
• Serve as champion for complex analytics transformations, ensuring technical feasibility, business value realization, and adoption.
• Drive culture change around data stewardship and accountability by embedding governance responsibilities into platform tooling and engineering workflows.
• Lead internal communities of practice, workshops, and code reviews to disseminate modern data practices.
• Mentor senior engineers across data and analytics engineering, elevating technical acumen and architectural judgment.
• Influence hiring and team design decisions, supporting the scaling of high-performing, and collaborative data teams.
• Represent the organization in external forums (conferences, meetups, technical alliances) and establish credibility as an industry thought leader.

Benefits

• Competitive Wages & Paid Time Off
• Stock Purchase Plan & 401k with Employer Contributions Starting Day One
• Medical, Dental, & Vision Insurance with Optional Flexible Spending Account (FSA)
• Team Member Health/Wellbeing Programs
• Tuition Educational Assistance Programs
• Opportunities for Career Growth

What This Job Offers

• Job Type: Full-time
• Career Level: Senior