Senior Data Engineer

Transcenda is a global provider of design and engineering services. We put people first and strive to be agents of change by building a better future through technology. We are dedicated to empowering organizations to rapidly scale, digitally transform, and bring new products to market.

Recognized by Newsweek as one of America’s greatest workplaces of 2025, Transcenda is home to 200+ engineers, designers, analysts, and advisors solving complex business challenges through technology. By approaching our work through a variety of cultures and perspectives, we take calculated risks to design and develop innovative solutions that will have a positive impact tomorrow.

Interesting Facts:

• Over 200 team members
• Fully remote — we let people work where they work best.
• We work with clients who value our opinion and thought leadership, and where we can make a meaningful contribution to architectural decisions, engineering decisions, and product decisions.
• We have a strong social agenda and promote diversity and inclusion, and participate in a variety of charity initiatives throughout the year.
• We have fun team-building activities.
• Since we are rapidly growing, the ability to grow and advance your career is available and at a fairly quick rate.

Must Haves:

• Strong experience with Python, Java, or other programming languages
• Advanced knowledge of SQL, including complex queries, query modularization, and optimization for performance and readability
• Familiarity with the modern data stack and cloud-native data platforms, such as Snowflake, BigQuery, or Amazon Redshift
• Hands-on experience with dbt (data build tool) for data modeling and transformations
• Experience with data orchestration tools, such as Airflow or Dagster

‍

Nice to Have:

• Experience with GitOps, continuous delivery for data pipelines
• Experience with Infrastructure-as-Code tooling (Terraform)

‍

Key Responsibilities:

• Design and build a data platform that standardizes data practices across multiple internal teams
• Support the entire data lifecycle
• Build and maintain integrations across data processing layers, including ingestion, orchestration, transformation, and consumption
• Collaborate closely with cross-functional teams to understand data needs and ensure the platform delivers value
• Document architectures, solutions, and integrations to promote best practices, maintainability, and usability