Principal Data And Analytics Engineer Job at O'Reilly Auto Parts

Job Description

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.

Job Responsibility

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

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
Education: Bachelor's Degree or Equivalent Level in Computer Science or related field
Experience: Experience enables job holder to deal with the majority of situations and to advise others (Over 3 years to 6 years)
Managerial Experience: Experience of general supervision of more junior colleagues (7 to 12 months)

Nice to have

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

What we offer

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

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