Top Analysis Reporting Jobs in Birmingham, AL

Builds and maintains data pipelines and datasets supporting analytics, AI, and modeling. Responsibilities include ingesting healthcare claims and lab data into Snowflake and AWS Iceberg, processing structured and unstructured sources, curating data for AI use cases, implementing medallion architectures, evaluating data quality, and contributing to pilot initiatives using SQL, Python, AWS Glue, Terraform, and Snowflake.

Build and operate trusted finance data products, pipelines, models, reconciliations, dashboards, and automation. Partner with Finance, Accounting, Data Engineering, Product, and Platform teams to resolve data issues, improve reporting, support financial close, establish data lake quality controls, and enable timely insights. Use SQL, Python, dbt, Spark, Airflow, and AI-assisted tools to deliver tested, traceable, production-ready solutions.

Collects and analyzes data from project, financial, maintenance, and field systems; builds KPI dashboards and recurring reports; identifies trends, risks, and opportunities; supports budgeting and forecasting; validates data with project and operations teams; and communicates actionable insights to leadership while improving reporting processes.

The Business Intelligence Engineer analyzes complex internal and external data to solve business problems and deliver actionable insights to decision-makers. Responsibilities include integrating, analyzing, summarizing, and presenting data; conducting root cause analysis; using SQL, SAS, Databricks, and advanced Excel; and supporting data-driven business decisions. This is a remote role with occasional travel for training or meetings.

Teaches online undergraduate and graduate data analytics courses, develops syllabi and lesson plans, prepares course materials, facilitates discussions, evaluates student work, provides feedback, and integrates responsible AI practices. The role requires maintaining course alignment with institutional objectives, supporting diverse learners, participating in faculty training, and applying current analytics methods, tools, and ethical data practices.

Build and maintain product data models, ELT pipelines, semantic layers, and self-service analytics assets. Partner with Product Engineering and Data Governance to define canonical data, certify metrics, document taxonomies, monitor data quality, and support expansion and upsell analytics. Translate business requirements into governed data products, communicate insights to technical and non-technical stakeholders, and ensure data tooling complies with federal frameworks including FedRAMP and DoD IL4/5.

Analyze and document raw healthcare data ingestion into the Bronze layer of a medallion data architecture. Responsibilities include data mapping, lineage documentation, quality monitoring, schema-drift detection, ingestion troubleshooting, and data-health dashboard development. The role collaborates with engineering and analytics teams to improve data accuracy, traceability, and operational excellence across large healthcare datasets.

Analyzes, validates, reconciles, and extracts healthcare data from disparate systems using Excel and SQL. Supports SaaS implementation projects, customer troubleshooting, reporting automation through SFTP, and communication of findings to technical and nontechnical stakeholders. The role also assists with data integrity, issue resolution, customer updates, and implementation progress tracking.

Analyzes servicing, collections, application funnel, and contact-center data to identify performance gaps and inform business decisions. Builds automated reports, partner data files, Snowflake and dbt models, and operations dashboards. Maintains metric definitions, audits models, reconciles partner data, and develops business-case analyses. Uses AI tools to improve analytics workflows and collaborates with data engineering, product, and business teams.

Build and maintain production data pipelines and foundational datasets for Market Intelligence. Automate survey ingestion, cleaning, joining, scheduling, monitoring, and reporting-layer integration. Develop repeatable queries, improve data quality practices, document assumptions, support reproducible outputs, and translate research needs into technical solutions. The role requires close collaboration with technical and nontechnical stakeholders and ownership of reliable data infrastructure.