Senior Analytics Engineer

5+ years of experience in data analysis or analytics engineering roles with a progressive increase in responsibility. Advanced proficiency in SQL. Experience with dbt and building well-structured DAGs of data models is a strong plus! Proficiency with Metabase or a similar BI tool. Proven data wrangling and modeling skills. Proficiency in Python. Experience with ETL tools or orchestration frameworks such as Dagster. Experience with cloud data platforms and scalable data architecture. Experience with Amplitude and Terraform is helpful, but not essential! Excellent problem-solving abilities. Strong communication skills. Natural collaboration and organizational skills. Expertise in data mining and analysis methods.

Lead efforts to define and build success metrics for Marketing and Product programs and campaigns that drive meaningful growth. Deliver well-defined, transformed, tested, documented, and code-reviewed datasets that enable confident decision making. Proactively analyze data and insights, and communicate clear opportunities to stakeholders to drive performance. Work with Product Managers to analyze customer trends and product performance to improve company OKRs. Conduct exploratory analysis to uncover new areas of opportunity, generate hypotheses, and quickly assess and clearly communicate the potential upside of a given opportunity. Manage the integration of tracking requirements for new products and features, and collaborate closely with cross-functional teams to ensure accurate data capture. Identify and integrate valuable data sources using ETL tools (e.g. Fivetran, Stitch) or custom pipelines in Python (we use Dagster). Collaborate with research and engineering teams to improve data models that feed business intelligence tools, increase data accessibility, and foster data-driven decision making across the organization. Implement processes and systems to monitor data quality, ensuring production data is always accurate and available for key stakeholders that depend on it. Develop and maintain scalable data pipelines and build out new API integrations to support growth in data volume and complexity. Define and maintain clear data documentation, definitions, and governance to ensure consistency across metrics like MAU and MRR. Translate analytical findings into strategic recommendations that drive product enhancements, customer satisfaction, and revenue growth.