• Participating in the design and development of Data solutions for challenging projects. • Develop projects from scratch with minimal supervision and strong team collaboration. • Be a key player in fostering best practices, clean, and reusable code. • Develop ETLs using Spark (Python/Scala). • Work on cloud-based projects (Azure/AWS). • Build scalable pipelines using a variety of technologies.

Role Description We are looking for a strong Analytics Engineer to take charge of data transformation and build a reliable foundation for our marketing analytics. You will work at the intersection of Data Engineering and Data Analytics, turning the "chaos" of API exports into structured data marts. What You Will Do - Design and Development: Build and maintain scalable data models in Snowflake and BigQuery using dbt. - Data Modeling: Translate marketing business logic (LTV, CAC, attribution, cohort analysis) into clean, optimized SQL code. - Pipeline Optimization: Ensure Data Quality and freshness; set up tests and documentation within dbt. - Tech Stack: Work with a modern Modern Data Stack. We use the best tools available for transformation and storage. - Collaboration: Work closely with Growth Hackers and Marketers to understand their data needs. Qualifications - SQL Mastery: You write complex, readable, and efficient queries. You know your window functions and understand the specific dialects of Snowflake/BigQuery. - dbt (Data Build Tool): Experience with dbt (Core or Cloud) is mandatory. You know what ref, macro, and incremental models are and why tests are necessary. - Cloud Data Warehousing: Deep understanding of Snowflake or Google BigQuery architecture. - Marketing Domain: Understanding metrics (ROAS, ROMI, Retention) and how ad platforms (FB, Google Ads) operate is a massive plus. - Engineering Mindset: Proficiency with Git, an understanding of CI/CD, and adherence to DRY (Don't Repeat Yourself) principles. Benefits - Reshape marketing with us. Let’s work! By submitting your application, you agree to our Privacy Policy.

• Lead and mentor a team of data engineers, fostering a culture of continuous learning, innovation, and operational excellence. • Partner with cross-functional teams to design and implement scalable data pipelines and models aligned with business needs and industry best practices. • Oversee the development, deployment, and maintenance of enterprise-grade ELT processes, data marts, and business intelligence assets. • Own and evolve data quality frameworks, ensuring proactive monitoring, troubleshooting, and resolution of data issues. • Champion the adoption of automation and modern data engineering practices, driving efficiencies in pipeline management and data delivery. • Guide your team in building domain knowledge around healthcare operations, ensuring data solutions comply with regulatory and security requirements. • Contribute to strategic planning for the data engineering function, including resource allocation, technology selection, and process improvement initiatives. • Support and enforce software development best practices, including code reviews, source control, CI/CD, and testing.

• Use data engineering best practices to produce high quality, maximally available data models which are intuitive to data analysts and trusted by stakeholders • Develop deep domain knowledge in healthcare operations, tracking regulatory developments related to analytics products you maintain • Apply quality measures and other metrics to datasets originating from internal and external clients • Build scalable ELT pipelines and business intelligence dashboards as needed, embracing automation wherever possible • Implement data quality checks which proactively identify data issues and distributional shifts to ensure accuracy of downstream analytical products