Senior Analytics Engineer

Analytics Engineering & Data Product Development

• Design, build, and maintain scalable analytics engineering solutions leveraging Databricks, Unity Catalog, and modern data platform capabilities.
• Develop and manage metrics-as-code frameworks, enabling standardized, governed, and reusable business metrics across product domains.
• Implement metric, cohort, segment, and KPI definitions within enterprise semantic layers and measurement platforms.
• Build and optimize Workstream Scorecards that connect strategic objectives, business drivers, performance indicators, and actionable insights.
• Develop automated data pipelines supporting metric refreshes, data validation, and reporting workflows.
• Create scalable analytics assets, reusable frameworks, and data products that accelerate business adoption and decision making.
• Establish best practices for data modeling, metric governance, version control, testing, and deployment. Partner with Data Engineering teams to define source data requirements, event tracking standards, and instrumentation strategies.
• Lead efforts to improve data quality, observability, lineage, and governance across analytics environments.

Product Analytics & Experimentation

• Partner with client stakeholders to translate business questions into measurable product KPIs and actionable insights.
• Develop product measurement frameworks focused on engagement, retention, acquisition, conversion, and customer lifecycle performance.
• Support experimentation initiatives through metric definition, experiment design consultation, and outcome measurement.
• Build and maintain customer cohorts, segmentation frameworks, and behavioral analysis models.

Engineering Excellence & Delivery Leadership

• Champion software engineering best practices within analytics development, including CI/CD, code reviews, testing, and release management.
• Manage Git-based development workflows and deployment pipelines.
• Document metric definitions, business logic, transformation rules, and data quality considerations.

Required Qualifications

• 4+ years of experience in Analytics Engineering, Product Analytics, Data Analytics, or Business Intelligence.
• Advanced SQL expertise with demonstrated experience analyzing large-scale event, clickstream, behavioral, or customer interaction data.
• Strong hands-on experience with Databricks, Unity Catalog, Delta Lake, and modern cloud data platforms.
• Experience with metrics-as-code and semantic layer technologies such as dbt, Cube, MetricFlow, or similar frameworks.
• Proficiency in Python for analytics, data processing, automation, and testing.
• Experience working with Git, CI/CD pipelines, code repositories, and software development lifecycle practices.
• Deep understanding of product analytics concepts including funnels, cohorts, DAU/WAU/MAU, retention, engagement, conversion, and customer journey analysis.
• Strong experience partnering with product, engineering, and business stakeholders to deliver analytical solutions.
• Excellent communication, consulting, and stakeholder management skills.
• Experience working in agile delivery environments and distributed teams.

Preferred Qualifications

• Experience supporting product experimentation and A/B testing programs.
• Exposure to AI/LLM evaluation frameworks, prompt engineering, or analytics copilots.
• Experience in media, streaming, digital subscription, or consumer-facing products.
• Familiarity with Tableau, Power BI, Looker, or similar visualization platforms.
• Experience with event-tracking tools such as Adobe Analytics, GA4, Mixpanel, Amplitude, or similar platforms.
• Knowledge of cloud ecosystems including AWS, Azure, or GCP.
• Prior consulting or client-facing delivery experience preferred.