Data Engineer

Insight Global; Sully Square (VA); On-site; USD 110,000 - 150,000; Full time

Job summary

Insight Global is expanding its Revenue Compliance data ecosystem and seeks a Data Engineer to design, build, and own data pipelines and models for analytics and AI initiatives. You will drive the migration to Amazon Aurora, ensuring seamless data movement and reporting accuracy across teams.

You’ll partner with engineers, DBAs, and stakeholders to translate ambiguous questions into concrete data requirements, implementing robust validation and monitoring in production.

Qualifications

• 4–8 years of data engineering experience with production ownership of pipelines.
• Advanced SQL with complex joins, window functions, and optimization.
• Strong Python for data engineering and scripting.
• Experience designing and operating ETL/ELT pipelines with orchestration.
• Data warehouse modeling and defendable schema designs.
• Hands-on cloud data platform experience (AWS preferred).
• Knowledge of relational databases (MySQL/PostgreSQL/SQL Server).
• Version control and CI/CD applied to data work.

Responsibilities

• Build and own data pipelines, moving data from transactional systems to analytics and reporting environments.
• Modernize the data platform, replacing legacy processes with maintainable, tested pipelines.
• Model data with warehouse schemas for consistent reporting across products.
• Support the Aurora migration ensuring downstream pipelines move cleanly.
• Own data quality with validation and alerting in pipelines.
• Prepare clean datasets for analytics and AI initiatives.
• Collaborate across teams to understand data meaning, not just where it lives.

Skills

Advanced SQL

Python for DE

ETL/ELT pipelines

Data warehouse modeling

AWS data platforms

RDBMS knowledge

Version control for data

Translate business questions

Data governance basics

Tools

dbt

Airflow

Dagster

Prefect

AWS Glue

Redshift

S3

Lambda

DMS

Job description

Job Description

About the Role

Our Revenue Compliance platforms generate a large and growing volume of tax, licensing, and compliance data. Today, too much of the work of moving, reconciling, and reporting on that data depends on legacy tooling and manual steps. We are hiring a Data Engineer to change that.

This is a foundational role. You will design and build the pipelines and data models that our reporting, analytics, and emerging AI initiatives depend on, and you will help retire the manual and legacy processes those teams rely on today. You will also support the data side of our migration to Amazon Aurora, making sure downstream reporting and extracts move cleanly with it.

This is a role for someone who wants to define how something is built rather than maintain someone else’s design. The scope is wide, the constraints are real, and the work is visible.

What You’ll Do

• Build and own data pipelines. Design, implement, and operate ETL/ELT pipelines that move data from transactional systems into analytical and reporting environments — reliably, on schedule, and with monitoring you can trust.
• Modernize the data platform. Replace legacy and manual data processes with maintainable, version-controlled, tested pipelines. Reduce the number of steps that require a person to remember something.
• Model data for consumption. Design warehouse schemas and data models that make reporting straightforward and consistent across products, instead of every report reinventing its own logic.
• Support the Aurora migration. Ensure downstream pipelines, extracts, and reporting move cleanly as source systems migrate; validate data integrity through cutover.
• Own data quality. Build validation, reconciliation, and alerting into pipelines so problems are caught before a customer or an auditor finds them.
• Enable analytics and AI. Partner with architecture and AI initiatives to prepare clean, well-structured, well-documented datasets that make downstream work possible.
• Partner across teams. Work with application engineers, the DBA, site reliability, and business stakeholders to understand what the data means — not just where it lives.
• Document and share. Maintain clear documentation of data sources, lineage, and definitions. As a senior contributor, mentor others and raise the team’s overall data practice.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to [e-mail] learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.

Skills and Requirements

• 4–8 years of data engineering experience, including production ownership of pipelines you built.
• Advanced SQL — complex joins, window functions, query optimization, and the judgment to know when a query is the wrong tool.
• Strong Python for data engineering (pandas, SQLAlchemy, or equivalent) and general scripting.
• Demonstrated experience designing and operating ETL/ELT pipelines, including orchestration, scheduling, error handling, and retry logic.
• Data warehouse and dimensional modeling experience; you can defend your schema design choices.
• Hands-on cloud data platform experience (AWS preferred; Azure or GCP considered).
• Working knowledge of relational databases — MySQL/MariaDB, SQL Server, or PostgreSQL.
• Version control and CI/CD practices applied to data work, not just application code.
• Ability to translate ambiguous business questions into concrete data requirements.
• AWS-native data tooling — Glue, DMS, S3, Redshift, Athena, Lambda, or Step Functions.
• Pipeline orchestration frameworks (Airflow, Dagster, Prefect) and transformation tooling such as dbt.
• Experience migrating reporting and analytics workloads alongside a database platform migration.
• Experience replacing legacy or low-code data tooling (Alteryx, SSIS, or similar) with engineered pipelines.
• Experience preparing data foundations for machine learning or AI use cases.
• Background in tax, financial services, government technology, or another regulated, audit-sensitive domain.
• Familiarity with data governance, lineage, and cataloging practices.