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

• Design, develop, and maintain scalable data pipelines using Python, PySpark, and other modern programming languages to support both batch and streaming workloads • Build and optimize data processing frameworks on cloud platforms such as Databricks or Snowflake, ensuring performance, reliability, and cost efficiency • Design and implement robust data models, including transactional (OLTP) and dimensional (OLAP) schemas, to support analytics, reporting, and application integration • Develop high quality SQL code including complex queries, stored procedures, and views, with a focus on performance tuning and efficient data access patterns • Create and manage workflow orchestration using Apache Airflow or similar tools, ensuring reliable scheduling, dependency management, and monitoring • Implement and enforce data governance and metadata standards through tools such as Microsoft Purview, including data lineage, classification, cataloging, and security policies • Build automated data quality and validation frameworks to ensure accuracy, completeness, and reliability of production datasets • Collaborate with cross functional teams including data architects, analysts, scientists, and business stakeholders to understand requirements and deliver scalable, well designed data solutions • Lead technical design sessions and code reviews, promoting engineering best practices, reusability, and maintainability • Support cloud infrastructure and DevOps practices, including CI/CD pipelines, version control, testing automation, and environment management • Monitor and troubleshoot production data pipelines, proactively addressing issues, performance bottlenecks, and system failures • Contribute to the evolution of the enterprise data platform, recommending tools, frameworks, and architectures to improve scalability and efficiency