Top Data & Analytics Jobs in Tulsa, OK

Owns go-to-market data governance, quality, enrichment, and automation across Salesforce and connected systems. Designs standards, controls, source-of-truth models, AI and LLM-powered workflows, monitoring, and remediation processes. Partners with Business Operations, Marketing Operations, Customer Success, Finance, IT, and security to improve data accuracy, compliance, reporting, and operational efficiency. Leads data management and automation projects from planning through measurable outcomes.

Leads the migration of legacy data warehouses and pipelines to an Azure and Databricks lakehouse. Defines technical architecture, reusable Azure Data Factory and Databricks pipelines, reconciliation methods, quality gates, and performance solutions. Mentors data engineers, reviews technical work, and collaborates with product, QA, platform, and customers in a remote Agile environment.

Analyzes marketing data to guide investment decisions, improve campaign performance, segment customers, support A/B testing, and identify conversion opportunities. Builds dashboards and reports, maintains metric definitions and data quality standards, investigates anomalies, and communicates actionable insights to sales, marketing, and global operations partners.

Delivers daily, weekly, and month-end customer reporting while maintaining solution components, validating data, and resolving reporting issues. Analyzes complex datasets, supports customer implementations, and improves reporting efficiency using Excel, Access, SQL, and the Infios FAP Portal. Collaborates with Operations, Product, Implementations, Commercial teams, and customers to provide accurate, data-driven outputs and document scalable processes.

Design and maintain Twilio’s R&D analytics data layer using AWS and analytics modeling tools. Define metrics and data lineage, build automated reconciliation and quality checks, investigate anomalies, and communicate solutions. Collaborate with Product and Engineering, mentor analytics team members, and establish best practices for data modeling, testing, documentation, and code review.

Performs audit-focused data extraction, analysis, and reporting using SQL, R, Python, SAS, Posit, and Snowflake. Supports internal and external audits, legal, compliance, and investigations by transforming complex datasets, identifying risks and trends, reviewing data models and process flows, and providing code feedback. Requires 5+ years of data analysis experience, a relevant bachelor’s degree, and strong querying and project management skills.

Develop and maintain data pipelines, datasets, dashboards, paginated reports, PowerApps solutions, Power Automate workflows, and Databricks data engineering processes. Translate ambiguous business needs into scalable analytics solutions, write complex SQL, support reporting and data modernization, and contribute to AI/ML initiatives. Collaborate with technical and non-technical stakeholders while delivering actionable insights and automated workflows.

Analyze case, client, and financial data to build Power BI dashboards, executive and client reporting, recurring Excel deliverables, SQL analyses, and lightweight automations. The role investigates discrepancies, supports ad hoc business questions, documents metric definitions, and partners with operations, sales, finance, and leadership to improve reporting and reduce manual work.

Coordinates Oracle EPM Cloud data governance, metadata management, integrity reviews, reconciliations, restatements, reporting controls, audit support, and system enhancements. Partners with Finance, Accounting, IT, and business stakeholders to resolve data variances, document requirements, support SOX compliance, maintain reporting outputs, and improve enterprise financial reporting processes.

Build and maintain dimensional data models in dbt and Snowflake for finance, operations, product, sales, and other business functions. Translate business questions into reliable data solutions, own model lifecycles, establish modeling and testing standards, optimize Snowflake performance and costs, collaborate with data engineering teams, review code, mentor colleagues, and communicate data definitions and tradeoffs to business leaders.