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 HR@insightglobal.com.

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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.