Top Entry Level Data & Analytics Jobs in Birmingham, AL

Loads, queries, extracts, validates, and refreshes client data; prepares reports and deliverables; identifies automation and process-improvement opportunities; supports UAT, system implementations, and quality assurance testing; and collaborates with cross-functional teams. The role translates client requirements into actionable tasks and requires SQL, relational database, Oracle, Unix, data analysis, reporting, and communication skills.

Reviews, develops, validates, and refines dashboards, reports, visualizations, calculations, data sources, KPIs, and analytical workflows across BI platforms. The role documents findings, provides structured feedback, troubleshoots reporting accuracy, and evaluates AI-powered agents and workplace connectors used in analytics processes.

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.

Performs accurate data entry, verification, updating, cleanup, organization, and maintenance across databases, spreadsheets, and digital systems. Maintains electronic records, converts documents into digital formats, prepares basic reports, retrieves information, tracks assignments, and supports scanning and filing. The role requires confidentiality, attention to detail, strong organization, dependable independent work, effective time management, and reliable remote-work capabilities.

Own data partner specification negotiations, data mapping, Ascend platform configuration, and internal QA across multiple implementations. Review file specifications, normalize schemas, validate test files, document data dictionaries, and route non-standard requests to Product or Customer Engineering. Coordinate with delivery and engagement teams on UAT, defects, readiness, and capacity constraints. Develop repeatable QA frameworks, implementation playbooks, procedures, and training materials while ensuring configurations meet quality standards before go-live.

Collect, clean, validate, and analyze operational data from multiple systems to support customer reporting and performance monitoring. Process SQL-based uploads, maintain accurate datasets, identify trends and anomalies, document reporting procedures, and create standardized workflows, dashboards, and tools. The role also supports process improvement and automation initiatives while collaborating with cross-functional teams and traveling to customer sites.

Designs, builds, and maintains scalable batch and streaming data pipelines and datasets in Databricks using Python, Spark, and SQL. Supports SQL Server, SSIS, SSRS, and data warehousing environments while improving performance, reliability, data quality, and scalability. Integrates enterprise data sources, enables reporting and AI/ML workloads, and partners with business and IT teams to deliver technical data solutions.

Designs, builds, and maintains scalable batch and streaming data pipelines and datasets in Databricks using Python, Spark, SQL, and Delta Lake. Supports SQL Server, SSIS, and SSRS environments while improving performance, reliability, data quality, and scalability. Integrates enterprise data sources, applies data warehousing practices, and partners with business users, analysts, and IT teams to deliver reporting, analytics, and AI/ML solutions.

Investigates data governance issues using information management tools and data sources, identifies root causes, and partners with data owners, clients, vendors, and architects on resolution. Establishes governance baselines, standards, metrics, audits, and reporting. Defines governance requirements for data elements, integrates governance tests into ETL processes, communicates governance benefits, and drives continuous improvement of data management systems, policies, and processes.

Lead and develop Analytics Engineers responsible for shared data foundations, platform capabilities, semantic models, developer tooling, and AI-enabled workflows. Define the team vision, operating model, roadmap, standards, and success measures. Partner cross-functionally to prioritize initiatives, improve adoption, guide architecture, and deliver scalable data assets and automation. Drive responsible AI-assisted development, data reliability, testing, documentation, observability, and cost management while contributing hands-on when needed.