Senior Data Engineer

Litera•Denver, CO

1d•Hybrid

About The Position

Litera is seeking a Senior Data Engineer to design, build, and operate a scalable analytics data platform that supports key business domains. This role focuses on developing high-quality data pipelines, a trusted Snowflake data warehouse, and well-modeled datasets that power analytics and decision-making across the organization. The engineer will work closely with analytics, engineering, and business stakeholders while owning data solutions end-to-end — from design through production.

Requirements

5+ years of experience designing, building, and operating production-grade data platforms, including scalable data pipelines and cloud data warehouses.
2+ years of production experience using dbt to build scalable, well-tested data warehouse solutions.
Strong SQL expertise, including complex analytical queries, performance optimization, and working with large datasets.
Hands-on experience with Snowflake (or similar cloud data warehouses) in a production analytics environment.
Strong proficiency in Python (or another object-oriented language) applied to data engineering, including writing maintainable, tested code.
Solid data engineering and software engineering fundamentals, including data modeling, version control (Git), testing, documentation, and collaboration with cross-functional stakeholders.

Nice To Haves

Advanced Snowflake expertise, including performance tuning, query profiling, and warehouse optimization.
Experience with dimensional and enterprise data modeling methodologies (e.g., Kimball, Inmon).
Experience owning analytics solutions for business domains such as Finance, Sales, or Marketing.
Advanced Python experience and/or multiple production dbt implementations with mature practices (testing, CI/CD, multi-environment deployments).

Responsibilities

Design, build, and maintain production-grade data pipelines and ELT workflows at scale.
Develop and own a Snowflake-based data warehouse using dbt best practices (models, tests, documentation, incremental strategies).
Partner with analytics and business stakeholders to translate requirements into well-modeled, reliable datasets.
Write and optimize complex SQL for large analytical datasets, ensuring performance and accuracy.
Apply strong software engineering practices to data work, including version control, testing, and CI/CD.
Integrate data from third-party SaaS platforms such as CRM, product analytics, and finance systems.
Produce clear technical documentation, including pipeline designs and data flow diagrams.
Take end-to-end ownership of data solutions, ensuring quality, performance, and long-term maintainability.