Top Analysis Reporting Jobs in Tulsa, OK

Lead enterprise data engineering standards, governed pipelines, data contracts, observability, quality controls, lineage, testing, and compliant data delivery. Partner with governance, privacy, architecture, modeling, and platform teams to ensure reliable, traceable, protected data products. Monitor operational performance, automate deployment and validation, and resolve pipeline and data reliability issues across complex enterprise environments.

The AVP, Data Engineer will design and build data ingestion, transformation, and replication pipelines using SQL, Python, dbt, and Dagster. Responsibilities include translating requirements into technical designs, developing data products, debugging production issues, supporting testing, and improving data engineering practices. The role also involves data modeling, mentoring junior engineers, conducting code reviews, and providing technical leadership while remaining hands-on. Experience with modern data platforms, workflow orchestration, cloud ecosystems, and insurance or financial services is preferred.

Leads enterprise data architecture for a federal transportation agency, including data modeling, data warehouses, data marts, database design, ETL/ELT integration, governance, and cloud modernization. The role architects AWS-based solutions, supports migrations to Databricks and data lakes, integrates disparate systems, and provides technical leadership to stakeholders and development teams.

Leads PBM data integration programs involving claims, eligibility, accumulator, and encounter data. Profiles, maps, validates, and reconciles data using advanced SQL; investigates production issues; designs scalable healthcare integrations; coordinates vendors, engineering, testing, UAT, migration, and deployment readiness; and manages timelines, risks, dependencies, and executive reporting. The role also maintains documentation and operational procedures while directing day-to-day integration work.

Develop and maintain Power BI reports, dashboards, visualizations, semantic models, and analytics solutions. Translate business requirements into scalable technical solutions, optimize queries and data models, ensure reporting accuracy and performance, and support data governance. Serve as the primary liaison for business users, provide training and troubleshooting, manage tickets, and collaborate with Data and QA teams. The role also advances AI and machine learning capabilities, mentors colleagues, and contributes to automation, documentation, and code reviews.

Provides senior-level data analysis, compliance reporting, qualitative and quantitative research, business intelligence, dashboards, and program measurement for the Personal Financial Counselor financial disclosure and conflict-of-interest program. Tracks certification, readiness, onboarding, and workforce metrics; identifies trends and risks; develops executive reports and actionable recommendations; supports audit readiness, process improvement, and cross-functional program decision-making.

Serves as the liaison between business stakeholders and ServiceNow developers, gathering requirements and mapping business processes to workflows. The role supports dashboards, KPIs, performance analytics, data validation, cleansing, transformation, testing, UAT, documentation, data governance, compliance, training, and process optimization. It develops user stories, acceptance criteria, and test scripts while helping federal agencies improve ServiceNow reporting, workflow automation, and operational performance.

Build and optimize data transformation pipelines using SQL, dbt, Airflow, Python, BigQuery, and relational databases. Collaborate with stakeholders and data engineers, manage task scheduling, resolve data issues, perform analyses, present recommendations, support data exploration tool adoption, and mentor team members. The role also contributes to hiring, onboarding, and professional development while ensuring data accuracy and insight quality.

Design, build, and maintain scalable big data pipelines, architectures, distributed databases, and storage systems. Use Python and frameworks such as Hadoop, Spark, or Flink for data integration, transformation, and processing. Ensure system performance, availability, security, governance, and data quality while collaborating with cross-functional teams and documenting technical solutions.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, improving data-processing systems for quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects through continuous improvement.