Top Analysis Reporting Jobs in Birmingham, AL

Lead technical advisor for enterprise data visualization and BI initiatives. Design semantic models and reporting architectures, implement Power BI and Microsoft Fabric solutions, optimize performance and security, run discovery workshops and executive advisory sessions, enable governed self-service and accessibility, and drive analytics adoption and AI readiness across clients.

Lead technical advisor for enterprise data governance engagements. Design governance strategy, metadata management, business glossaries, taxonomy, data lineage, stewardship models, and Microsoft Purview implementations. Run workshops, maturity assessments, and executive advisory sessions. Create governance monitoring, operational reporting, and AI-readiness assessments for platforms like Microsoft Fabric, Power BI, and Azure.

Senior Data Architect contractor to accelerate and modernize the data platform: assess landscape, design end-to-end data solutions, define integration and metadata patterns, enable data mesh and cloud migration (AWS), and produce reusable architecture artifacts for handoff to engineering teams.

The Data Engineer will develop and maintain data pipelines, perform data extraction and transformation, and support data analytics across the enterprise.

Analyze business data, build and maintain business models, provide actionable insights and reports, collaborate with cross-functional teams, monitor performance, and recommend efficiency improvements to support strategic decision-making.

Analyze large healthcare datasets to extract insights, build data models, reports and dashboards, ensure data quality, collaborate with cross-functional teams, and present findings to stakeholders.

Analyze large healthcare datasets, build and implement quantitative models, perform statistical analyses, ensure data accuracy, collaborate with cross-functional teams, and provide actionable insights to improve healthcare delivery.

Support building and maintaining ETL/ELT data pipelines and cloud data workflows. Work with senior engineers and cross-functional teams to implement, test, deploy, and document data processes. Continuously learn cloud technologies, SQL, and Python/PySpark, follow coding best practices, and contribute to process improvements in an Agile environment.

Design, build, and maintain scalable data pipelines and ETL/ELT frameworks using Python and PySpark. Develop reusable components, microservices, and cloud-based data solutions on AWS and Snowflake. Ensure code quality via reviews, CI/CD, logging, and best practices while collaborating with stakeholders to solve data problems.

Lead customer-facing data engineering and warehousing initiatives on the Databricks platform. Architect and optimize production-scale ETL/ELT pipelines, performance-test workloads, migrate legacy systems to cloud lakehouse architectures, support Solution Architects in technical sales, and provide training, demos, and community contributions.

Partner with customers and sales to design and present scalable Databricks Lakehouse architectures, build reference architectures and demos, consult on big data, ETL, and ML projects, validate integrations, and drive technical wins and adoption across accounts.

Client-facing BI consultant responsible for end-to-end delivery of TARGIT-based analytics: requirements gathering, prototyping, backend work, dashboards, training, deployment, optimization, and customer engagement.

Support a core set of clients by guiding users on Numerator's Insights platform, performing ad-hoc/custom analyses using proprietary consumer panel data, delivering actionable analytic insights and recommendations, and presenting findings across channels including industry forums and written content.

Lead GTM strategy and analytics to build headcount/capacity models, territory optimization, quota setting, predictive models, and BI reporting. Partner with Sales, Marketing, Finance, and Data to inform planning cycles, monitor pipeline and productivity, and deliver executive-level insights for growth and investment decisions.

Build and maintain scalable ETL/data pipelines and internal frameworks to support ML and product teams. Drive architecture, observability, data quality, and engineering best practices across AI/ML workflows and prompt-engineering pipelines.