Design, build, and operationalize scalable analytics engineering solutions on Databricks and modern cloud platforms. Implement metrics-as-code and semantic layers, create automated pipelines, enable product measurement and experimentation, define cohorts and KPIs, improve data quality and governance, and lead engineering best practices and stakeholder delivery for data products.
The ideal candidate combines deep technical expertise in analytics engineering with strong product analytics acumen and stakeholder management skills. You will work closely with client Product Managers, Data Engineers, Data Scientists, and business leaders to design, develop, and operationalize modern analytics solutions that improve product measurement, experimentation, and business outcomes.
As an EXL consultant, you will play a critical role in translating business objectives into scalable data products, ensuring analytical rigor, data quality, and operational excellence across the analytics ecosystem.
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.
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