Top Remote Data & Analytics Jobs in Birmingham, AL

Design, build, and operate production data pipelines and shared data foundations using SQL, Python, PySpark, Databricks, and cloud technologies. Develop data models, ingestion and transformation workflows, resilient processing, observability, data-quality controls, and secure customer-aware data flows. Collaborate with engineering and product teams, evaluate architecture and tooling, support analytics and application services, contribute to testing and documentation, and mentor other engineers.

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.

Designs Azure-based data models, simulations, test environments, automated testing frameworks, and performance validation solutions for healthcare data pipelines. Develops test data generators, validates ETL and HL7 FHIR transformations, supports data quality and reliability, and collaborates with engineers and architects on design and code reviews. The role also provides technical guidance on testing architecture, CI/CD, infrastructure as code, resiliency, and large-scale cloud data processing.

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.

Evaluate enterprise data architectures, governance frameworks, integration patterns, ownership models, and data quality practices. Design target-state architectures supporting analytics and AI, define governance operating models, assess data flows, develop quality controls, and create strategic roadmaps and executive recommendations. Collaborate with business and technology stakeholders on data modernization, cloud migration, AI enablement, and enterprise data management initiatives.