• Build the Data Platform • Design, build, and maintain scalable ELT/ETL pipelines integrating product data, payment providers, and third-party APIs • Architect and own the company's cloud data warehouse (Snowflake, BigQuery, or Redshift) • Build reliable orchestration workflows using Airflow, Prefect, or similar tools • Optimize warehouse performance and cost through efficient data modeling and query optimization • Ensure Data Quality & Governance • Develop clean, testable transformation layers using dbt or equivalent frameworks • Design a semantic layer that provides consistent business metrics across the organization • Implement data quality testing, monitoring, lineage, and documentation • Build security and governance into the platform, including access controls, PII handling, and privacy-aware data practices • Partner Across Engineering & Product • Work closely with Product and Growth teams to support product analytics, experimentation, subscription metrics, and business reporting • Collaborate with software engineers on event tracking, data contracts, and API integrations • Promote DataOps best practices, including CI/CD, version control, testing, and documentation-as-code

• Responsible for transforming raw data into clean, reliable, and well-documented models that power analytics. • Design, build, and maintain transformation layers in dbt, following best practices for data modeling. • Own the semantic and metrics layer, defining governed, version-controlled business metrics. • Partner with BI developers to expose trusted datasets through BI tools. • Build and maintain documentation, data dictionaries, and lineage for stakeholders. • Own end-to-end data validation by building automated tests into the transformation workflow. • Collaborate on operationalizing analytics within services such as Snowflake Cortex and Databricks AI. • Mentor junior analysts and engineers in SQL and dbt best practices.

• Actively engage with business stakeholders to understand context, challenges, and underlying needs; translate complex requirements into clear, detailed technical specifications that enable effective use of AI and analytics platforms. • Take full responsibility for the data products you build — from ingestion through modeling, transformation, validation, to final delivery and ongoing monitoring. • Implement and maintain rigorous validation processes, automated testing, and quality checks; act as guardian of data accuracy and integrity, ensuring metrics and KPIs accurately reflect business reality. • Design and implement data transformation layers that pre-aggregate business information, apply consistent business rules, and deliver datasets ready for consumption in dashboards and AI applications. • Collaborate with AI teams to specify and prepare structured data, data dictionaries, and models that enable AI platforms and intelligent reporting capabilities. • Build and maintain data ingestion pipelines from multiple sources, implement transformations in SQL and Python, and ensure performance and scalability of solutions. • Go beyond fulfilling requirements — proactively identify improvement opportunities, anticipate internal customer needs, propose innovative solutions, and challenge approaches when necessary. • Deliver reliable data solutions with speed and precision, balancing the need for rapid iteration with the highest quality standards. • Articulate technical concepts clearly to non-technical audiences; facilitate alignments, present solution proposals, and document modeling and architecture decisions.

• Senior Data Developer (Analytics Engineer) who serves as a bridge between business and technology • Own the complete lifecycle of data products – from ingestion and modeling through final delivery • Ensure that data is structured, validated, and ready for consumption in AI and analytics environments • Actively engage with business stakeholders to understand context, challenges, and underlying needs • Translate complex requirements into clear, detailed technical specifications that enable effective use of AI and analytics platforms • Take full responsibility for the data products built – from ingestion through modeling, transformation, validation, to final delivery and ongoing monitoring • Implement and maintain rigorous validation processes, automated testing, and quality checks to ensure data accuracy and integrity • Design and implement data transformation layers that pre-aggregate business information • Collaborate with AI teams to specify and prepare structured data, data dictionaries, and models that enable AI platforms and intelligent reporting capabilities • Build and maintain data ingestion pipelines from multiple sources, implement transformations in SQL and Python • Proactively identify improvement opportunities, anticipate internal customer needs, and propose innovative solutions • Deliver reliable data solutions with speed and precision, balancing the need for rapid iteration with high quality standards • Articulate technical concepts clearly to non-technical audiences, facilitate alignments, present solution proposals, and document modeling and architecture decisions