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Senior Manager Data Analytics Jobs in Springfield, MO

Bass Pro Shops

Sr Manager, Risk Management

Springfield, MO · On-site

$150 - $210/hr

## Sr Manager, Risk ManagementApplylocations: Springfield, MO (Bass Pro Shops Base Camp)time type ... Analyze claims data and loss trends to identify root causes, recommend operational changes to ...

Ra

Marketing Analytics Manager

Buffalo, MO · On-site

Mid - Senior Level This is what you will do.. You will be using quantitative methods to assess the ... Who has nearly 5+ years of experience in the analysis of Marketing data using SAS and other ...

O'Reilly Auto Parts

Sr. Manager of Product

Springfield, MO · On-site

$119K - $157K/yr

The Sr. Manager, Product Management/Business Analyst leads the articulation of current and future ... from data that is collected to assist in compiling reports that will help in decision-making.

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Senior Manager Data Analytics information

See Springfield, MO salary details

$40.9K

$104.1K

$153.3K

How much do senior manager data analytics jobs pay per year?

As of Aug 24, 2026, the average yearly pay for senior manager data analytics in Springfield, MO is $104,127.00, according to ZipRecruiter salary data. Most workers in this role earn between $85,000.00 and $122,800.00 per year, depending on experience, location, and employer.

What does a senior manager data analytics do?

A Senior Manager Data Analytics leads teams that analyze large sets of data to help organizations make informed business decisions. They develop analytics strategies, oversee data projects, and ensure the quality and integrity of data-driven insights. This role often involves collaborating with other departments, mentoring analysts, and presenting key findings to senior leadership. Senior Managers in this field need strong technical skills, leadership abilities, and business acumen to drive impactful results.

What are the key skills and qualifications needed to thrive as a senior manager data analytics?

To thrive as a Senior Manager Data Analytics, you need advanced expertise in data analysis, statistical modeling, and business intelligence, typically supported by a degree in a quantitative field and several years of analytics experience. Proficiency with analytics tools such as SQL, Python, R, and platforms like Tableau or Power BI, as well as experience with data warehousing systems, is essential. Strong leadership, strategic thinking, and communication skills enable you to guide teams and translate complex findings into actionable business insights. These competencies are crucial for driving data-informed decision-making and maximizing organizational value from analytics initiatives.

How does a senior manager data analytics typically collaborate with cross-functional teams within an organization?

Senior Managers of Data Analytics frequently work alongside cross-functional teams such as IT, product development, marketing, and finance to ensure that data-driven insights align with business objectives. They are responsible for translating complex analytical findings into actionable recommendations and communicating these insights clearly to both technical and non-technical stakeholders. Regular collaboration often involves leading meetings, setting project priorities, and ensuring data initiatives are integrated smoothly with ongoing business strategies. This collaborative environment fosters innovation and helps drive organizational growth.

What is the difference between Senior Manager Data Analytics vs Data Analyst?

AspectSenior Manager Data AnalyticsData Analyst
Required CredentialsBachelor's/Master's in Data Science, Analytics, or related field; extensive experienceBachelor's degree in related field; entry to mid-level experience
Work EnvironmentLeadership roles, strategic planning, team managementData collection, analysis, reporting
Employer & Industry UsageCorporate, finance, healthcare, tech companiesVarious industries, including marketing, finance, tech
Common Search & ComparisonOften compared for leadership and strategic rolesCompared for technical and analytical skills

The main difference between Senior Manager Data Analytics and Data Analyst lies in their responsibilities and experience level. Senior Managers focus on strategic oversight, team leadership, and decision-making, while Data Analysts handle data collection, analysis, and reporting at a more technical level. Senior Managers typically have more experience and credentials, working in leadership roles within organizations across various industries.

What are popular job titles related to Senior Manager Data Analytics jobs in Springfield, MO?

For Senior Manager Data Analytics jobs in Springfield, MO, the most frequently searched job titles are:

What job categories do people searching Senior Manager Data Analytics jobs in Springfield, MO look for?

The top searched job categories for Senior Manager Data Analytics jobs in Springfield, MO are:

What cities near Springfield, MO are hiring for Senior Manager Data Analytics jobs?

Cities near Springfield, MO with the most Senior Manager Data Analytics job openings:

Senior Manager Data Analytics jobs near you

Manager Data and Analytics Engineering- (GCP/Snowflake)

Springfield, MO • On-site

O'Reilly Auto Parts
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Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 15 days ago

Job description

The Manager, Data and Analytics Engineering leads a team of engineers responsible for delivering scalable, secure, and high-performing data platforms, pipelines, and analytics solutions across business domains. This role drives end-to-end execution of data initiatives, ensuring high standards of engineering excellence, governance, and delivery discipline.
As a Team Member leader, the Manager is accountable for building and developing technical talent, fostering a culture of innovation and ownership, and aligning team efforts with enterprise priorities. The ideal candidate combines strong engineering experience with business acumen, stakeholder partnership, and a continuous improvement mindset to accelerate the impact of data and analytics across the organization. This role serves as a key bridge between engineering execution and strategic delivery, guiding the team in building well-governed, high-impact data assets that support cross-functional analytics, decision automation, and AI readiness.
This position is located in Springfield, MO. Remote work is not an option for this role.
Responsibilities and Duties:
  • Provide hands-on leadership in the design, development, and deployment of enterprise-grade data platforms, batch and streaming pipelines, semantics layer and analytics-enabling services.
  • Ensure the team follows best practices in data engineering, architecture patterns (e.g., medallion, data mesh), and platform-specific optimization (e.g., Snowflake, BigQuery, dbt, Airflow, Prefect).
  • Guide implementation of secure, cost-efficient, and reusable data products, frameworks, and interfaces across ingestion, transformation, semantics and delivery layers.
  • Promote adherence to CI/CD, observability, schema management, and infrastructure-as-code practices for resilient data product deployment.
  • Own the successful delivery of data initiatives, balancing technical feasibility, scope, timelines, and stakeholder expectations.
  • Establish delivery plans, resource plans, sprint cadences, and engineering KPIs to monitor progress, unblock teams, and ensure predictable outcomes.
  • Collaborate with product owners, business stakeholders, and program teams to define roadmaps, resource needs, and prioritization of data products and platform enhancements.
  • Serve as the escalation point for engineering blockers, architectural decisions, or trade-off discussions, driving resolution across teams.
  • Ensure team compliance with enterprise data modeling, documentation, and metadata standards.
  • Standardize technical documentation practices for data models, transformation logic, and platform operations to promote reuse and transparency.
  • Embed lineage, data dictionary, platform metadata integration, and architectural documentation into delivery workflows using tools such as Alation, Collibra, and schema registries.
  • Partner with governance, compliance, and security teams to integrate policy-as-code frameworks, RBAC, and data governance policies into engineering execution.
  • Drive implementation of data quality frameworks embedded within orchestration and transformation pipelines.
  • Establish SLAs, observability dashboards, and automated validation rules for critical data assets and domain-specific pipelines.
  • Lead root cause analysis and continuous improvement for data quality incidents, latency, pipeline failure, ensuring traceability across ingestion, enrichment, and delivery layers.
  • Collaborate with technology and business teams to operationalize trusted data practices and ensure alignment on quality definitions and expectations.
  • Contribute to shaping data domain strategy by aligning engineering execution to enterprise priorities and architectural principles.
  • Partner with product, technology, business and architecture leaders to define roadmaps that advance data maturity, platform scalability, and solution interoperability.
  • Champion platform evolution initiatives such as self-service enablement, AI/ML readiness, and composable data product design.
  • Provide input to the enterprise architecture council on patterns, trade-offs, and emerging technologies to guide platform modernization.
  • Build trusted relationships with product owners, domain leaders, and business stakeholders enterprise domains such as across marketing, supply chain, customer, and store operations.
  • Present project status, technical trade-offs, and platform health to both technical and non-technical audiences with clarity and confidence.
  • Represent engineering in business domain forums, roadmap sessions, providing insight into data platform capabilities, gaps, and enhancement opportunities.
  • Oversee the delivery of foundational data assets, curated datasets, and semantic layers to drive business outcomes and analytics adoption.
  • Guide the team in building unified metrics layers, semantic data models, and analytical datasets aligned with business reporting and decisioning needs.
  • Partner with data science and ML engineering to ensure the semantic layer and metric stores are consistent, reusable, and extensible for downstream use cases.
  • Oversee integration of business logic into transformation layers to support real-time, self-service, and LLM-driven analytics.
  • Enable rapid insights through well-structured feature stores, reusable dbt models, and aligned dimensional views.
  • Lead change management efforts during platform migrations, architectural shifts, and new technology onboarding.
  • Drive cultural adoption of modern practices such as declarative data modeling, data contracts, universal semantic layer, streaming-aware designs, and platform-as-a-product mindset.
  • Stay informed on emerging data technologies to shape future-state capabilities and ensure team readiness for evolution.
  • Define and drive team development plans aligned with evolving platform and domain requirements, ensuring skill growth and succession planning.
  • Foster a culture of engineering excellence, agile delivery, collaboration, and accountability across the team.
  • Lead internal communities of practice, technical workshops, and user groups to drive and disseminate best practices.

Skills:
Required:
  • Google Cloud Platform knowledge and experience
  • Strong experience leading the design, development, and deployment of enterprise-grade data platforms and products (e.g., Snowflake, BigQuery, dbt, Airflow, Informatica, and Kafka/PubSub).
  • Proficient in programming languages such as Python and SQL with ability to review code, enforce engineering standards, and support performance optimization in batch and streaming pipelines.
  • Demonstrated understanding of data architecture principles, including medallion layering, data mesh design, and streaming architectures to support diverse use cases.
  • Experience implementing CI/CD for data pipelines, schema management workflows, and observability frameworks (e.g., data quality monitors, alerting dashboards, SLOs/SLAs).
  • Practical experience managing transformation and orchestration layers to deliver curated data assets, semantic layers, and ML feature stores aligned to consumption patterns.
  • Proficiency in metadata-driven engineering, including integration with Alation, Collibra, or similar tools for lineage, data catalogs, and policy enforcement.
  • Proven ability to collaborate with product owners, business stakeholders, and domain SMEs to prioritize data needs, translate requirements into executable engineering tasks, and ensure business value delivery.
  • Deep understanding of enterprise business processes across functional areas (e.g., supply chain, customer, store ops, finance) and how data platforms can accelerate decision-making.
  • Ability to evaluate business cases, clarify problem statements, and manage trade-offs between delivery timelines, data quality, performance, and platform sustainability.
  • Experience in leading the delivery of trusted, governed data assets that directly impact business reporting, operational workflows, and digital transformation initiatives.
  • Demonstrated leadership in managing data engineering teams, including capacity planning, performance management, skill development, and career growth.
  • Experience managing sprint cycles, engineering backlogs, and delivery KPIs in collaboration with cross-functional partners to ensure timely and predictable delivery of data products.
  • Ability to lead by example, mentor engineers, and enforce technical standards and architecture patterns across the team.
  • Skilled at facilitating architecture reviews, design sessions, and engineering forums to drive consensus and technical decision-making.
  • Experience serving as an escalation point for resolving platform blockers, resourcing risks, and architecture trade-offs with engineering and business leaders.
  • Experience aligning engineering execution with enterprise data strategy, including platform modernization, AI readiness, and semantic unification.
  • Experience supporting initiatives like universal semantic layer design, KPI harmonization, and enabling consistent, reusable metric definitions across the business.
  • Strong competence in partnering with architecture, governance, and platform teams to ensure delivery aligns to broader enterprise blueprints, privacy policies, and compliance frameworks.
  • Ability to promote platform improvements and automation opportunities (e.g., lineage, schema evolution, versioned metadata, self-service enablement).

Preferred:
  • Experience implementing or governing open table formats (e.g., Iceberg) and cloud-agnostic data exchange architectures for hybrid, multi-cloud scale and interoperability.
  • Familiarity with dbt modular modeling, metrics layer design, and streaming-aware semantic patterns that support real-time insights and ML model training pipelines.
  • Proven track record leading or contributing to cloud migration initiatives, architectural modernization, or platform consolidation programs in enterprise environments.
  • Exposure to policy-as-code, RBAC frameworks, and data security automation integrated into engineering workflows.
  • Ability to drive change management and cultural adoption of engineering practices such as data contracts, declarative modeling, and streaming delivery.
  • Experience contributing to technical documentation standards, onboarding playbooks, and internal engineering communities of practice.

O'Reilly Auto Parts has a proven track record of growth and stability. O'Reilly is full of successful career stories and believes in a strong promote-from-within philosophy, encouraging you to grow your career along with the organization.
Total Compensation Package:
  • 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

O'Reilly Auto Parts is an equal opportunity employer. The Company does not discriminate on the basis of race, religion, color, national origin or ancestry (including immigration status or citizenship), sex, sexual orientation, gender identity, pregnancy (including childbirth, lactation, and related medical conditions,) age (40 and over), veteran status, uniformed service member status, physical or mental disability, genetic information (including testing or characteristics) or another protected status as defined by local, state, or federal law, as applicable.
Qualified individuals with a disability may be entitled to reasonable accommodation under the Americans with Disabilities Act. If you require a reasonable accommodation during the application or employment process, please send an email to: rar@oreillyauto.com or call (800) 471-7431 option , and provide your requested accommodation, and position details.

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