Apply on Employer Site

O'Reilly Auto Parts · 7 hours ago

Principal Data and Analytics Engineer

Springfield, MO

Full-time

Onsite

Lead/Staff

$110K/yr - $165K/yr

O'Reilly Auto Parts is a company with a proven track record of growth and stability. They are seeking a Principal Data and Analytics Engineer to lead the design and implementation of the organization's enterprise-wide data infrastructure and analytics capabilities, influencing executive leadership on data innovation and driving the long-term data strategy.

AutomotiveManufacturingRetailWholesale

H1B Sponsor Likely

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

Enable intelligent operations and decisioning by translating unstructured business logic into structured knowledge artifacts, such as KPIs, rulesets, feature stores, and semantic models used by dashboards or AI agents

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

Qualification

Enterprise-scale data platformsCloud-native environmentsData governance frameworksCI/CD pipelinesAdvanced SQLPythonData quality managementSemantic modelingOrchestration frameworksAnalytical skillsLeadershipCommunication skillsProblem-solving

Required

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

Preferred

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 explainability

Communication and presentation skills with the ability to convey complex architectural decisions to both technical and non-technical stakeholders

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

Company

O'Reilly Auto Parts

Glassdoor3.3

O’Reilly Auto parts is a specialty retailer of automotive aftermarket parts, tools, supplies, equipment and accessories.

Founded in 1957

Springfield, Missouri, USA

10001+ employees

http://www.oreillyauto.com/

H1B Sponsorship

O'Reilly Auto Parts has a track record of offering H1B sponsorships. Please note that this does not guarantee sponsorship for this specific role. Below presents additional info for your reference. (Data Powered by US Department of Labor)

Distribution of Different Job Fields Receiving Sponsorship

Represents job field similar to this job

Trends of Total Sponsorships

2024 (1)

2021 (2)