Litera
Tech / AI / Software
SeniorDataEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Data Engineer at Litera. Skills: Data pipeline development, Snowflake data warehouse, dbt, SQL, Python, Data modeling. 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).”
What You'll Achieve.
Support key business domains with a scalable analytics data platform.; Power analytics and decision-making across the organization.; Ensure quality, performance, and long-term maintainability of data solutions.
Industry & Context.
What They're Looking For.
Must Have
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., 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., 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 Have
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).
What You'll Do.
and maintain production-grade data pipelines and ELT workflows at scale.
Develop and own a Snowflake-based data warehouse using dbt best practices (models
incremental strategies).
Write and optimize complex SQL for large analytical datasets
ensuring performance and accuracy.
Integrate data from third-party SaaS platforms such as CRM
Take end-to-end ownership of data solutions
and long-term maintainability.
How You'll Work.
Team & Collaboration
Partner with analytics and business stakeholders to translate requirements into well-modeled, reliable datasets.; Apply software engineering practices to data work, including version control, testing, and CI/CD.; Collaboration with cross-functional stakeholders.
Full Job Description
_**Job Description**_ ** _Ready to Help Shape the Future of Legal Tech?!_** At Litera, we don’t just build software, we transform how the world’s top law firms operate. Every day, we Raise The Bar™️ for what’s possible through AI, innovation, and solutions that power millions of legal professionals worldwide. If you’re energized by scale, real impact, and meaningful challenges, you’ll feel right at home here. ** _Where You’ll Work_** **Hybrid:** This is a hybrid role based in Denver, Colorado with the expectations to be in office at least 3 days a week for collaboration and connection. ** _Why this Role Matters_** We’re looking for a Senior Data Engineer to help 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. You’ll work closely with analytics, engineering, and business stakeholders while owning data solutions end‑to‑end — from design through production. ** _What You’ll Deliver_** * 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. ** _What You’ll Bring
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