Data Engineer / Analyst

aquesst•Woodstock, GA

12d•$120,000 - $140,000•Onsite

About The Position

We are hiring a hands-on Data Engineer with strong analytical skills to help build and support a custom analytics platform used for internal decision-making. This role focuses on transforming raw, multi-source data into reliable datasets, applying analytical logic, and delivering clear reporting that supports strategic and operational insights. This is a highly practical role for someone who enjoys working close to the data, improving reliability, and turning complex inputs into usable outputs for non-technical stakeholders.

Requirements

Strong experience using Python, R, or PowerShell for data processing and analysis.
Solid command of SQL, including writing and optimizing queries.
Experience building dashboards or reports using tools such as Excel, Power BI, or Tableau.
Comfort working in an on-premise data environment.
Strong analytical mindset with close attention to data quality and consistency.
Clear written and verbal communication skills.
Ability to work independently and take ownership of deliverables.
Strong experience using Python, including building data integrations and processing pipelines.
Hands-on experience creating dashboards in Power BI. Candidates with Tableau experience are also welcome.
Solid working knowledge of relational databases, specifically PostgreSQL.
Demonstrated experience with data analysis and translating raw data into meaningful insights.
Strong SQL skills, including writing and optimizing complex queries.
Comfortable working in an on-premise environment with no cloud-based infrastructure.
Proven ability to help build or support an internal analytics platform or application, including developing data pipelines, integrating data from multiple sources, and applying analytical models.
Experience working in smaller, lean teams with shifting priorities and evolving requirements.
Strong communication skills with the ability to work directly with business stakeholders and explain data-driven findings clearly.

Nice To Haves

Background working with real estate, housing, land, or market-based datasets.
Familiarity with location-based or geospatial data concepts.
Exposure to financial, pricing, or scenario-based analytical models.
Experience combining data from multiple third-party and manually sourced inputs.

Responsibilities

Data Engineering and Pipeline Development Build, maintain, and improve data pipelines using tools such as Python, R, or PowerShell.
Ingest and standardize structured data into relational databases including PostgreSQL, MySQL, or SQL Server.
Design data models that combine multiple raw inputs into usable datasets such as inventory tracking, transactional activity, and external market indicators.
Ensure data accuracy through validation checks, reconciliation processes, and scheduled refreshes.
Create clear schemas and documentation to support long-term usability and ownership.
Analytics and Data Modeling Support internally developed analytical logic related to inventory levels, pricing behavior, absorption trends, and market performance.
Produce repeatable analyses that deliver consistent and explainable results.
Partner with business users to translate real-world assumptions into data-driven inputs.
Test and validate outputs using historical comparisons and source-level verification.
Reporting and Visualization Build and maintain dashboards for internal users.
Create focused visualizations that clearly surface trends, risks, and opportunities.
Ensure reports are intuitive for non-technical audiences and aligned to specific business questions.
Respond to ad hoc analysis requests with speed and accuracy.
Data Collection and Validation Work with internal teams and external sources to confirm data accuracy and completeness.
Conduct limited direct outreach by email or phone when clarification or validation is required.
Integrate manually collected or field-sourced data into standardized workflows.
Collaboration and Process Improvement Work closely with stakeholders to refine data requirements and reporting needs.
Identify opportunities to reduce manual effort and improve reliability across workflows.
Manage multiple priorities while meeting agreed timelines with minimal oversight.