Position: Senior Data & Analytics Engineer Location: Atlanta, Georgia
Duration: Contract
Job ID: 179089
Pay Range: $58/hr to $62/hr
Enterprise data engineering and SQL 1. Strong ability to source, join, transform, validate, and document data across large enterprise platforms like Snowflake, Palantir, Databricks, and SQL-based warehouses.
2. Data quality troubleshooting and root cause analysis Ability to investigate discrepancies, missing data, duplicates, mapping issues, refresh problems, and transformation failures - then define sustainable remediation steps
3. Data requirements mapping Skill in translating business needs into source-to-target mappings, field definitions, business rules, data models, validation criteria, and technical specifications.
4. AI/agent data enablement Ability to prepare trusted, structured, business-ready datasets for Copilot Studio agents, Microsoft Copilot, Databricks AI/BI, dashboards, retrieval, and workflow automation.
5. Cross-functional communication and collaboration Ability to work with developers, business stakeholders, data owners, transformation partners, and platform teams while explaining technical data topics clearly to non-technical audiences.
KEY RESPONSIBILITIES: Position Summary - We are seeking a Senior Data & Analytics Engineer to join a small, autonomous team focused on delivering business value through AI, analytics, automation, and enterprise productivity tools. This role will serve as a key data enablement resource for AI agent development, reporting initiatives, workflow automation, and transformation efforts.
- The successful candidate will source data from enterprise warehouses, troubleshoot and resolve data quality issues, map data requirements for new initiatives, and prepare trusted data assets that support Copilot Studio agents, Databricks workflows, dashboards, and partner-led solutions.
- This position is ideal for a data professional who can navigate large enterprise data environments, understand business data needs, work across multiple tasks simultaneously, and collaborate effectively with developers, delivery leads, business stakeholders, and partner organizations.
Key Responsibilities Enterprise Data Sourcing & Platform Navigation - Identify, access, and source data from enterprise warehouses, analytical platforms, operational systems, and approved data repositories.
- Work across Snowflake, Palantir, Databricks, SQL-based platforms, and related enterprise data environments to locate and prepare required data.
- Develop datasets, views, tables, pipelines, extracts, and reusable data assets that support AI agents, reporting, automation, and business initiatives.
- Partner with data owners and technical teams to understand data availability, ownership, lineage, refresh cadence, definitions, and access requirements.
- Create practical, well-documented data assets that can be reused by developers, analysts, agents, dashboards, and partner teams.
Data Quality Troubleshooting & Remediation - Investigate, troubleshoot, and resolve data quality issues as they arise across source systems, transformations, datasets, dashboards, and agent data sources.
- Perform root cause analysis on data discrepancies, missing records, duplicate values, field mismatches, transformation failures, and reporting inconsistencies.
- Develop validation routines, reconciliation checks, monitoring logic, and data quality controls that improve reliability and reduce repeat issues.
- Coordinate with source system owners, platform teams, developers, and business partners to correct data defects and communicate impacts.
- Document data issues, remediation steps, assumptions, business rules, validation results, and decisions for future reference.
Data Requirements Mapping & Solution Support - Gather, clarify, and document data requirements for new initiatives, agent builds, reporting solutions, automation opportunities, and partner-led projects.
- Translate business needs into source-to-target mappings, field definitions, transformation rules, data models, validation criteria, and technical specifications.
- Identify data gaps, access constraints, quality concerns, governance considerations, and platform dependencies early in the initiative lifecycle.
- Support solution design discussions by advising on available data, appropriate sources, integration paths, and readiness for downstream use.
- Maintain data dictionaries, mapping documents, metadata notes, and requirement traceability for key datasets and initiatives.
AI Agent Data Layer Enablement - Support the data layer required for Microsoft Copilot, Copilot Studio, AI agents, dashboards, and automation solutions.
- Prepare trusted, structured, and business-ready datasets that can be used for agent grounding, retrieval, recommendations, reporting, and workflow support.
- Partner with AI developers to ensure agent solutions are connected to accurate, relevant, and maintainable data sources.
- Assist with testing agent outputs where data accuracy, metadata, field mapping, or source logic affects the user experience.
- Help define repeatable patterns for sourcing, validating, and maintaining data used by AI-enabled solutions.
Cross-Functional & Partner Support - Work closely with AI developers, technical delivery managers, analysts, business stakeholders, and other members of the autonomous team.
- Provide data expertise to internal initiatives and support partner organizations when dotted-line assistance is needed.
- Coordinate with transformation partners, reporting teams, data platform teams, and business users to resolve data questions and support delivery timelines.
- Participate in solution reviews, testing sessions, user validation, issue triage, and implementation readiness discussions.
- Communicate technical data topics clearly to both technical and non-technical audiences.
Analytics & Reporting Support - Develop and maintain analytical datasets, operational reports, dashboards, and ad hoc analysis to support business decision-making.
- Assist with KPI definition, metric validation, data interpretation, and reporting consistency across initiatives.
- Support executive reporting and partner updates by providing accurate, explainable data and analysis.
- Recommend improvements that increase data transparency, reduce manual reconciliation, and improve reporting reliability.
- Use AI and automation tools where appropriate to accelerate analysis, documentation, validation, and issue resolution.
Required Qualifications - 3+ years of experience in data analysis, data engineering, analytics engineering, business intelligence, data management, or a related technical data role.
- Strong SQL development skills and experience working with enterprise data warehouses or large-scale analytical environments.
- Experience sourcing, joining, validating, transforming, and documenting data from multiple enterprise platforms.
- Experience troubleshooting data quality issues and performing root cause analysis on data discrepancies.
- Experience gathering and documenting data requirements, data mappings, field definitions, business rules, and validation criteria.
- Working knowledge of data modeling, metadata, data lineage, data governance, ETL/ELT, and reporting concepts.
- Ability to manage multiple concurrent tasks, respond to emerging data issues, and operate effectively in a fast-moving environment.
- Strong communication and collaboration skills with the ability to work across technical teams, business stakeholders, and partner organizations.
Preferred Qualifications - Hands-on experience with Snowflake, Palantir, and Databricks.
- Current expertise with Databricks AI capabilities, including Genie Agents/Genie Spaces, dashboard apps and AI/BI experiences, agent-enabled analytics workflows, and MCP server integrations for governed enterprise data access.
- Experience supporting AI agents, Copilot Studio, Microsoft Copilot, automation workflows, or AI-enabled analytics solutions.
- Experience with Python, PySpark, notebooks, REST APIs, JSON, CSV, parquet, Delta tables, or similar data engineering technologies.
- Experience with Power BI, Microsoft Fabric, Azure Data Services, SharePoint, Power Platform, or enterprise reporting platforms.
- Experience creating data quality dashboards, reconciliation workflows, validation scripts, or exception reporting.
- Experience working with partner teams, transformation organizations, data platform teams, or cross-functional delivery groups.
- Experience in Agile, rapid prototyping, operational analytics, or business transformation environments.
Characteristics of a Successful Candidate - Enjoys solving complex data problems and improving data reliability.
- Can navigate large enterprise data environments and quickly identify useful data sources.
- Understands that strong AI solutions depend on trusted, well-documented, and accessible data.
- Is comfortable supporting multiple initiatives and responding to emerging data issues as they arise.
- Works well in a small, autonomous team where initiative, adaptability, and ownership are essential.
- Can collaborate effectively with developers, delivery leads, business partners, and dotted-line partner teams.
- Communicates data findings, risks, and requirements clearly to both technical and non-technical audiences.
Why Join Our Team Our team is small, autonomous, and focused on delivering value quickly through AI, analytics, automation, and commonly available enterprise tools. We build practical solutions that help business partners work more effectively and make better decisions.
This role is critical to the foundation of our AI and agent development work. By improving data access, quality, mapping, and readiness, this position helps ensure that our agents, dashboards, and automation solutions are accurate, trusted, and useful.
About PTR Global: PTR Global is a leading provider of information technology and workforce solutions. PTR Global has become one of the largest providers in its industry, with over 5000 professionals providing services across the U.S. and Canada. For more information visit
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