Python Analytics Engineer, Solace

• Strong Python skills, especially in the context of data transformation and workflow automation
• Advanced SQL expertise with experience designing and optimizing data models in Snowflake or Postgres
• Hands-on experience with modern data orchestration tools like DBT and Airflow
• Track record of building analytics-ready datasets, semantic layers, or curated data marts
• Comfort working cross-functionally with product, analytics, and engineering teams
• Familiarity with internal BI and dashboarding frameworks like Streamlit or Dash

Desirable

• As a Python Analytics Engineer at Solace, you’ll sit at the intersection of engineering, analytics, and product—owning the systems and data models that turn complex healthcare data into actionable insight
• You’ll partner closely with analysts, data scientists, and engineers to build robust data pipelines, semantic layers, and curated data marts that enable powerful self-serve analysis across the company
• You are an engineer who thinks like an analyst and operates like a product builder
• You have a deep understanding of data modeling and workflow automation, and you thrive on making data more usable, reliable, and discoverable
• Whether you’re optimizing SQL, automating workflows with Python, or building analytics-ready tables in Snowflake, you take pride in delivering clean, trusted data that powers decision-making
• Collaborate with analysts, engineers, and business leaders to design scalable data models and workflows
• Build and maintain analytics-ready datasets, data marts, and semantic layers in Snowflake and Postgres
• Write production-quality Python code to automate workflows, data validation, and ETL orchestration using tools like DBT and Airflow
• Optimize SQL transformations and ensure the accuracy, usability, and transparency of data assets
• Partner with infrastructure teams to uphold data quality, integrity, and discoverability across the stack
• Support internal dashboard development with tools like Streamlit or Dash
• Contribute to data engineering best practices—version control, code review, documentation, and CI/CD for analytics workflows