Data Analyst / Analytics Engineer

As a Data Analyst / Analytics Engineer at CookieYes, you will play a crucial role in managing the data infrastructure to ensure reliability, scalability, and actionable insights across teams. You will be responsible for implementing, maintaining, and extending data pipelines, warehouses, and BI systems to empower Product, Growth, and Finance teams to make data-informed decisions. Key Responsibilities: - **Data Infrastructure Ownership** - Maintain and enhance a unified data architecture. - Manage data ingestion from sources like MySQL, Stripe, Mixpanel, and other product APIs. - Monitor pipeline health, resolve data discrepancies, and ensure accuracy. - Manage schema documentation, data lineage, and update transformations as per evolving business needs. - **Business Intelligence & Reporting** - Own and maintain dashboards in Power BI, Looker Studio, or Fabric for cross-functional stakeholders. - Support business reporting across various metrics like MRR, ARPU, churn, LTV, and funnel performance. - Build and automate self-serve dashboards for Product, Growth, and Finance teams. - Collaborate with leadership to define KPIs and create impactful visualizations. - **Product & Behavioral Analytics** - Maintain product tracking setup in Mixpanel or equivalent, ensuring consistent event taxonomy and validation. - Collaborate with Product and Engineering teams to track feature usage, adoption, and retention metrics. - Ensure alignment between product analytics and revenue data for comprehensive insights. - **Data Governance & QA** - Maintain event taxonomy, naming conventions, and data ownership per domain. - Implement QA checks for key metrics and proactively flag anomalies. - Ensure compliance with GDPR and privacy-by-design principles in data handling. - **Collaboration & Continuous Improvement** - Partner with Product, Engineering, and Growth teams to identify new data needs and enable experimentation. - Document data processes and contribute to knowledge-sharing across teams. - Drive best practices for analytics reliability, validation, and reporting consistency. Required Skills & Experience: - 3-5 years of experience in Data Analytics, BI, or Analytics Engineering, preferably in B2B SaaS. - Proficiency in SQL, including joins, CTEs, window functions, and query optimization. - Hands-on experience with ETL tools and data warehouses like BigQuery, Redshift, Snowflake, etc. - Skills in Power BI, Looker Studio, or other BI tools for dashboarding and automation. - Strong communication skills to explain data concepts to non-technical stakeholders. Nice to Have: - Experience with Python for automation, transformation, or API integrations. - Knowledge of data validation frameworks and monitoring tools. - Familiarity with product analytics platforms like Mixpanel, Amplitude, etc. - Exposure to PLG or freemium SaaS models and experimentation frameworks. - Familiarity with privacy and compliance requirements such as GDPR, CCPA.,