Top Remote Analysis Reporting Jobs in Atlanta, GA

Analyze and design enterprise healthcare data solutions using Snowflake and advanced SQL. Perform data profiling, reconciliation, anomaly detection, source-to-target validation, and quality testing across claims, membership, provider, and health plan data. Support data architecture, dimensional modeling, governance, metadata, lineage, cataloging, and MDM initiatives. Prepare data for AI and advanced analytics, translate business requirements into solutions, resolve data issues with stakeholders, and communicate actionable insights while following security and HIPAA requirements.

Analyze business, financial, customer, product, market, and operational data to identify trends and growth opportunities. Maintain KPI, OKR, profitability, and executive reporting dashboards, prepare leadership and Board materials, and develop actionable insights for Sales and Client Success teams. Use data to improve retention, expansion, efficiency, profitability, and customer recommendations while partnering across finance, operations, sales, and executive leadership.

Lead retention and marketing analytics: build lifecycle and segmentation frameworks, design and analyze A/B tests, drive personalization and KPI dashboards, automate retention reporting, and partner with Marketing, CRM, Product, and Data Engineering to turn insights into action.

As a Lead Data Engineer, you'll optimize data workflows, manage data engineering projects, mentor junior engineers, and ensure robust data solutions meet business needs.

Develop and maintain reports, dashboards, KPIs, and executive insights using SQL, Power BI, and other BI tools. Reconcile operational and financial data, investigate anomalies, ensure reporting quality, and document data models, metadata, governance, and reporting inventories. The role also performs advanced data manipulation, visualization, data storytelling, and analytical recommendations. Fraud and disputes domain knowledge is preferred.

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.

Build scalable Tableau dashboards and reporting systems using operational data from Salesforce, AWS, SQL databases, APIs, and vendor platforms. Analyze large datasets, resolve data-quality issues, identify performance drivers and emerging risks, and translate findings into actionable recommendations. Prepare business-review insights for senior leaders, standardize metrics, improve reporting efficiency, and partner cross-functionally to support customer experience and operational decisions.

Lead business analytics strategy by developing dashboards, scorecards, KPIs, automated reporting, and performance measurement frameworks. Analyze sales and operational data to identify trends, risks, and opportunities, translating findings into actionable recommendations. Partner with Sales, Operations, Finance, IT, and leadership on forecasting, business cases, analytics modernization, governance, and process improvement. Communicate insights through reporting and data storytelling while mentoring analysts and leading cross-functional initiatives.

Develops, tests, deploys, and maintains enterprise-facing data orchestration software that automates retail merchandising decisions. Collaborates with data scientists, product leaders, engineers, and business analysts to build cloud-deployed applications and data solutions. Participates in agile processes, follows engineering best practices, supports production applications, and continually develops technical and leadership skills.