Top Remote Analysis Reporting Jobs in Atlanta, GA

As a Data Engineer, you will develop solutions for customer-centric technology, ensuring platform integration and managing data architecture. Responsibilities include building data processes, maintaining data integrity, and collaborating cross-functionally to meet business challenges.

Short-term, paid project to export and submit personal financial data from multiple apps. Participants must provide exports from specific financial categories, create two financial artifacts, redact PII on-device using a provided tool, and follow quality and timeline requirements until Final Approval.

Lead enterprise analytics architecture and strategy across Epic Cogito, BI tools, and cloud platforms. Design scalable, secure solutions, enforce RBAC and DevOps/CI-CD practices, mentor federated teams, guide tool selection, and ensure alignment with business priorities. Provide architectural oversight, stakeholder consultation, and adoption planning for analytics initiatives in a large healthcare system.

Leads advanced data analysis to guide product and business decisions, develops metrics, dashboards, data products, and AI-enabled reporting systems, and translates findings into strategic recommendations. Partners with data engineers on dimensional data models, data governance, and quality controls. Mentors analysts, aligns roadmaps with business priorities, improves problem framing, and communicates insights effectively to technical and business stakeholders.

Design, build, and maintain Scala-based ETL and streaming data pipelines, APIs, and data microservices in Azure. Optimize LLM fine-tuning and RAG workflows, support DevOps/CI-CD practices, document standards, mentor engineers, and manage data ingestion frameworks for analytics and partner integrations.

Design and build Power BI reports and SSRS reports, write advanced SQL to extract and validate data, create scalable data models, investigate trends, automate reporting processes, and collaborate with business partners to translate requirements into usable analytics solutions.

Designs, deploys, and supports healthcare data acquisition, integration, transformation, and data quality solutions using enterprise and cloud technologies. Develops SQL assets, OMOP Common Data Model transformations, testing processes, documentation, and AI data pipelines. Manages projects, resolves escalated incidents, mentors data engineers, supports training, and collaborates with analysts, vendors, clients, and technology teams.

Build and maintain performance dashboards, KPIs, and data frameworks for revenue operations. Analyze funnel performance and program effectiveness, translate complex data into leadership insights, automate recurring reporting, and perform SQL-based data wrangling and transformations. The role partners with cross-functional, engineering, and data science teams to improve measurement, resolve data issues, and support strategic business decisions.

Own paid social measurement and analytics across brand, consideration, and performance campaigns. Execute Conversion Lift and Brand Lift Studies, design geo-randomized experiments with data science, build KPI dashboards, evaluate creative and audience performance, and recommend budget and strategy changes. The role requires advanced SQL and Python or R, causal inference expertise, scalable reporting, and the ability to communicate insights to senior marketing leaders.

Implement, QA, and document tag management and data-layer specs across GTM, GA4, server-side pipelines, Shopify, and native apps. Collaborate with product and engineering to ensure tracking, pixel integrity, attribution, and experimentation event fidelity; support monthly audits, consent enforcement, and maintain centralized solution design documentation.

Owner of data platform modernization: design and migrate legacy systems to Kimball-style dimensional models and star schemas; build reliable production data pipelines; enforce data hygiene and validation; champion dbt best practices; mentor junior engineers; collaborate with analysts and stakeholders to ensure scalable, well-documented data models and platform observability.