Data Engineer

Insight Global; San Antonio (TX); On-site; USD 120,000 - 180,000; Full time

Job summary

Insight Global is seeking a Senior Data Engineer to join a growing data and analytics team. You will build and optimize data pipelines, transforming raw operational data into high-quality datasets in a modern cloud data warehouse environment, while collaborating with business and technical teams.

The ideal candidate has 4+ years in ETL/ELT, strong SQL, 3+ years DBT and Snowflake, and experience with Git Bash in Linux.

Qualifications

• 4+ years of hands-on data engineering experience with ETL/ELT pipeline development.
• 4+ years SQL proficiency including complex queries and data warehouse concepts.
• 3+ years DBT (SQL, YAML, Jinja); DBT Fusion experience preferred.
• 3+ years Snowflake as a cloud data warehouse platform.
• Git Bash commands on Linux to interact with GitHub/GitLab.
• Experience with relational data modeling concepts: star schema, snowflake schema, PK/FK.
• Experience with sensitive or regulated data; Python is a strong plus.

Responsibilities

• Design, build, and maintain ETL/ELT data pipelines using DBT in Snowflake.
• Develop data models with relational concepts (star/snowflake schemas, fact/dimension tables).
• Collaborate across technical and business teams to align data products with requirements.
• Mentor mid- and junior-level data engineers and guide project delivery.

Job description

We are seeking a skilled Senior Data Engineer to join a growing data and analytics team at a leading financial services organization. This role is focused on building and optimizing data pipelines that support member experience initiatives — transforming raw operational data into consumable, high-quality datasets within a modern cloud data warehouse environment. The ideal candidate will bring strong ETL expertise, solid relational data modeling fundamentals, and excellent verbal and written communication skills to collaborate effectively across technical and business teams.

Key Responsibilities

• Design, build, and maintain ETL/ELT data pipelines using DBT (Data Build Tool) to transform raw source data into consumable data models within Snowflake
• Develop and manage data models using relational modeling concepts including star schema, snowflake schema, fact and dimension tables, and primary/foreign key relationships
• Interact with GitHub and GitLab via Git Bash commands on a Linux terminal to pull branches, develop locally, and push code back to shared repositories
• Leverage AI-assisted development tools locally within the data engineering toolbox, ensuring code quality and accuracy before committing to the common code repository
• Write and optimize complex SQL queries and YAML/Jinja templates as part of DBT development workflows
• Handle sensitive and regulated data with appropriate data quality and data scrubbing practices in compliance with organizational standards
• Support multiple concurrent data projects under the member value organization, including Flowable process data and complaints data initiatives
• Mentor and provide technical guidance to mid- and junior-level data engineers, taking initiative to lead portions of projects and seek out additional work when bandwidth allows

Must-Have Requirements

• 4+ years of hands-on data engineering experience, with a strong foundation in ETL/ELT pipeline development
• 4+ years of strong SQL proficiency, including complex query writing, optimization, and data warehouse concepts
• 3+ years of experience with DBT (Data Build Tool) — including SQL, YAML, and Jinja templating — with DBT Fusion experience strongly preferred
• 3+ years of experience with Snowflake as a cloud data warehouse platform
• Proficiency with Git Bash commands for interacting with GitHub and/or GitLab via a Linux terminal (pulling from master, working in feature branches, pushing code)
• Demonstrated understanding of relational data modeling concepts: star schema, snowflake schema, fact/dimension tables, Cartesian products, and primary/foreign key relationships
• Experience working with sensitive or regulated data and applying data quality best practices; Python experience is a strong plus