Description:
Who we are:
Veteran Benefits Guide (VBG) was founded by a former United States Marine with the goal of ensuring that Veterans receive accurate disability benefits in a timely manner. Since it was founded, VBG has guided more than 35,000 Veterans through the complicated Veteran Affairs (VA) disability claims process. As a company founded by a Veteran and staffed by many Veterans and families of Veterans, VBG is committed to advocating for policies that protect the rights and interests of former service members.
Who we’re looking for:
The Data Analytics Engineer is responsible for transforming raw and staged data into trusted, well-modeled, and analytics-ready datasets that empower reporting, dashboards, and data-driven decision-making across the organization. This role bridges the gap between engineering and analysis — ensuring data is clean, consistent, connected, and optimized for use by Analysts, BI Developers, and business teams.
You will work closely with Data Engineers (who ingest data), BI Developers (who build dashboards), and Analysts (who generate insights) to build the semantic layer of the warehouse. You will own data modeling, cleansing, deduplication, and constructing unified datasets that bring together information from systems such as Salesforce, NetSuite, Google, and internal applications.
This position is open to candidates located in the following states: Arizona (AZ), California (CA), Washington (WA), Nevada (NV), Utah (UT), Illinois (IL), Ohio (OH), New Jersey (NJ), Virginia (VA), North Carolina (NC), and Florida (FL).
Essential Functions:
Reasonable accommodation may be provided to enable individuals with disabilities to perform essential functions.
Data Modeling & Transformation
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Build, maintain, and optimize curated data models using SQL, dbt, or similar transformation tools.
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Create dimensional models (fact/dimension) and semantic layers to support reporting and advanced analytics.
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Construct unified datasets that bring together cross-system information (e.g., Salesforce, NetSuite, Google Ads).
Data Quality & Reliability
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Profile, validate, and cleanse data to eliminate duplicates, missing fields, and inconsistencies.
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Implement automated data tests to ensure accuracy, completeness, and referential integrity.
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Investigate and resolve issues flagged by Analysts when metrics do not match or data looks incorrect.
Warehouse Optimization & Governance
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Partner with DBAs and Data Engineers to ensure performance at the warehouse structures and optimized queries.
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Adhere to and help define data governance, documentation standards, and semantic layer best practices.
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Maintain version-controlled analytics codebases using Git or similar workflows.
Collaboration & Stakeholder Support
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Work closely with Analysts to understand their data needs and translate them into robust models.
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Support BI Developers by providing clean, reliable datasets that power dashboards and reports.
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Communicate issues, improvements, and data model changes clearly to technical and non-technical audiences.
Success Measures (KPIs)
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Reduction in analyst time spent cleaning and prepping data (target: 40–60% reduction).
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Decrease in recurring data mismatches or report inconsistencies.
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Increased adoption of curated datasets by Analysts and BI Developers.
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Faster turnaround time for new data model requests and enhancements.
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High data quality scores and reduction in manual remediation efforts.
Requirements:
Qualifications or competencies:
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Advanced SQL skills (window functions, CTEs, performance tuning).
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Experience with transformation frameworks (dbt strongly preferred).
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Strong understanding of data warehousing concepts: star schema, snowflake schema, fact/dimension modeling.
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Familiarity with cloud warehouses (Snowflake, BigQuery, Redshift, Synapse).
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Ability to troubleshoot mismatched metrics, broken joins, or duplicated data.
Preferred Skills
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Experience with Python or R for data validation or automation scripts.
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Knowledge of BI tools (Power BI, Tableau, Looker) and how they interact with semantic layers.
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Familiarity with CI/CD for analytics code and version control (Git).
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Exposure to data governance, cataloging, and documentation tools.
Education and previous work experience:
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Bachelor’s degree in Data Analytics, Computer Science, Information Systems, or related field.
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3–5+ years of experience in analytics engineering, BI development, or data modeling.
Qualifications or competencies:
-
Advanced SQL skills (window functions, CTEs, performance tuning).
-
Experience with transformation frameworks (dbt strongly preferred).
-
Strong understanding of data warehousing concepts: star schema, snowflake schema, fact/dimension modeling.
-
Familiarity with cloud warehouses (Snowflake, BigQuery, Redshift, Synapse).
-
Ability to troubleshoot mismatched metrics, broken joins, or duplicated data.
Preferred Skills
-
Experience with Python or R for data validation or automation scripts.
-
Knowledge of BI tools (Power BI, Tableau, Looker) and how they interact with semantic layers.
-
Familiarity with CI/CD for analytics code and version control (Git).
-
Exposure to data governance, cataloging, and documentation tools.
Education and previous work experience:
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Bachelor’s degree in Data Analytics, Computer Science, Information Systems, or related field.
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3–5+ years of experience in analytics engineering, BI development, or data modeling.
EEO:
Veteran Benefits Guide provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, national origin, ancestry, physical disability, mental disability, medical condition, marital status, sex (including pregnancy, childbirth, breastfeeding or related medical conditions), gender (including gender identity and gender expression) genetic characteristic, sexual orientation, registered domestic partner status, age, military or veteran status, hairstyle or hair texture, reproductive health decision making, or any other characteristic protected by federal, state, or local laws.
