REMOTE Sr Data Engineer

Required Skills & Experience

-5+ years of experience as a Data Engineer building and supporting production data pipelines, data modeling, data transformations and data optimization -Experience with cloud platforms (AWS, Azure, GCP) and modern data architectures -Strong proficiency in Python, SQL, and Git within modern data environments -Experience with cloud data platforms and processing frameworks (Snowflake or Databricks, Spark) -Hands‑on experience with analytics engineering tools such as dbt or Coalesce -Experience using DAG‑based orchestration tools (Airflow or Dagster) and data quality frameworks -Strong problem‑solving, communication skills, and curiosity about how data impacts business outcomes, with exposure to finance/insurance/annuity data

Nice to Have Skills & Experience

• this role is EST hours- candidates preferred in CST/EST time zones

Job Description

Insight Global is seeking a Data Engineer to join a growing data and analytics team supporting a leading insurance and financial services organization. This individual will be responsible for building and maintaining scalable data pipelines, optimizing data workflows, and ensuring high-quality, reliable data is available across the business. The ideal candidate has experience working within modern cloud data environments and enjoys solving complex data challenges while partnering with stakeholders to drive business outcomes. Benefit packages for this role will start on the 1st day of employment and include medical, dental, and vision insurance, as well as HSA, FSA, and DCFSA account options. 401k retirement account access with employer matching will start after 90 days if elected . Employees in this role are also entitled to paid sick leave and/or other paid time off as provided by applicable law.

Pay range $60-75/hr

Key Responsibilities

Design, build, and support production-grade data pipelines and ETL/ELT processes. Develop and maintain data models, transformations, and architecture to support reporting, analytics, and operational use cases. Optimize data performance, quality, reliability, and scalability across enterprise data platforms. Build and manage data workflows using orchestration tools such as Airflow or Dagster. Implement data quality checks, monitoring, and governance best practices. Collaborate with analytics, engineering, and business teams to translate requirements into scalable data solutions. Support cloud-based data platforms and contribute to the evolution of modern data architecture. Partner with stakeholders to understand data needs and deliver solutions that support business objectives.