• Analyze large-scale national Medicaid datasets (including T-MSIS or related program data) using SQL and Python within a Databricks environment. • Analyze datasets to understand their structure, relationships, and intended use cases, ensuring they logically fit within the broader data ecosystem. • Develop high-level usage examples, dashboards, and visualizations (using Amazon QuickSight or Power-BI) to translate complex data into actionable insights for policy evaluation and operational decision-making. • Create, improve, and maintain data dictionaries and metadata; evaluate existing documentation to identify gaps or inconsistencies in business meaning and referential integrity. • Work closely with data engineers, architects, and policy stakeholders to align data with business context, improve data accessibility, and enhance analytical workflows. • Help teams understand how to combine datasets to answer complex analytical questions and lead the development of innovative solutions for program-level problems. • Proactively identify opportunities to enhance the data platform's analytical capabilities and document standard design methodologies for reuse across the program.

Tiger Analytics is looking for experienced Lead Data Analyst to join our fast-growing advanced analytics consulting firm. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world. We are looking for a Lead Data Analyst with strong experience in Python, SQL, ETL processes, and cloud technologies. The ideal candidate will have hands-on experience working with SQL, data analysis, and consulting, and performing data analysis to support business insights and decision-making. This role requires someone who is comfortable working with large datasets, transforming data, and collaborating with cross-functional teams in a fast-paced environment. Key Responsibilities - Partner with business stakeholders to understand and translate business needs into solution requirements. - Write efficient SQL queries to extract, manipulate, and analyze data from multiple sources. - Use Python for data processing, automation, and analytics. - Work with AWS /GCP/ Azure cloud services to manage and analyze data in cloud environments. - Perform data validation, quality checks, and troubleshooting for data pipelines. - Build reports, dashboards, and insights to support business decisions. - Facilitate large stakeholder groups and manage the complete requirements lifecycle. - Conduct structured analysis using both quantitative and qualitative techniques. - Profile data manually and document transformation logic and rules. - Produce clear and concise Source to Target Mapping Documents with detailed Data Mapping Requirements. - Collaborate effectively within an Agile environment. - Collaborate with data engineers, analysts, and business stakeholders to understand requirements. - Ensure data accuracy, consistency, and governance across systems.

Senior Data Analyst (Growth & Analytics Engineering) Job Type: Full-Time Location: 100% Remote Role Overview In your first phase, you will act as a senior analytics partner to Growth, Product, and Lifecycle teams. You will translate ambiguous questions into structured analysis, deliver clear recommendations, and help shape experimentation and channel strategy. As you ramp, you will assume greater ownership of the analytics layer in our modern data stack. This includes designing, refactoring, and maintaining dbt models that define our core business metrics, improving metric consistency across BI tools, and strengthening the reliability and scalability of reporting. You will collaborate closely with Data Engineering (pipeline and infrastructure) and Data Science (advanced modeling and forecasting), while owning the analytical semantic layer that connects raw data to decision-making. Key Responsibilities Insight & Business Impact - Own growth and lifecycle analytics across paid acquisition, web funnel, and retention. - Analyze Shopify, Amplitude, and marketing channel data to surface actionable insights that influence budget allocation and experimentation strategy. - Lead experimentation analysis (A/B testing, conversion rate optimization, landing page performance). - Develop and maintain executive-ready dashboards and reporting in Looker (with migration to Omni underway). - Partner with stakeholders to define clear success metrics and ensure decisions are grounded in consistent definitions. Analytics Engineering & Data Foundation - Design, refactor, and maintain core data marts in dbt to ensure metric consistency and scalable reporting. - Standardize key business definitions (e.g., CAC, LTV, retention, repeat rate) and enforce alignment across teams. - Improve data quality through QA, reconciliation, and proactive anomaly detection across marketing, commerce, and attribution sources. - Collaborate with Data Engineering on upstream model design and with Data Science on downstream analytical use cases. - Contribute to the evolution of our analytics layer as the company scales. Required Qualifications - 5–8 years of experience in analytics, business intelligence, or a related quantitative role, at least 2+ years supporting eCommerce or DTC businesses. - Advanced SQL proficiency (Snowflake preferred) with the ability to independently build complex queries and analytical datasets. - 3+ years of hands-on experience in growth or marketing analytics, including paid media performance analysis and customer acquisition economics. - Experience working with web or product analytics tools (Amplitude or similar) to analyze funnel and behavioral data. - Demonstrated experience designing and analyzing experiments (A/B testing, CRO). - 2+ years of experience working with dbt or similar transformation frameworks, with practical understanding of dimensional modeling and metric layer design. - Experience building and maintaining dashboards in BI tools (Looker or Omni preferred). - Proven ability to independently manage ambiguous requests and translate them into structured analyses. - Strong written communication skills in a remote, asynchronous environment. - Ability to provide partial overlap with US Eastern Time for key alignment meetings (full-day overlap not required). Preferred Qualifications - 7–10 years of total experience in analytics, including ownership of analytical data models in a modern data stack. - Experience in significantly refactoring or owning analytical data marts. - Familiarity with attribution methodologies (including MTA) and reconciling platform-reported vs modeled data. - Experience with tools such as Fivetran or Airbyte. - Experience partnering cross-functionally in high-growth consumer brands. - Comfort collaborating closely with Data Engineering and Data Science in a shared analytics organization. What Success Looks Like - Within the first 6 months, you are driving high-leverage insights across Growth and Lifecycle while building credibility as a strategic thought partner. - Within 12 months, you have meaningfully improved the structure and reliability of our core data marts, clarified metric definitions across teams, and reduced friction in reporting and decision-making. - This role is suited for someone with sufficient depth to operate independently from day one, while progressively shaping the analytics foundation that supports the business long term.

Senior Data Analyst (Growth & Analytics Engineering) Job Type: Full-Time Location: 100% Remote Role Overview In your first phase, you will act as a senior analytics partner to Growth, Product, and Lifecycle teams. You will translate ambiguous questions into structured analysis, deliver clear recommendations, and help shape experimentation and channel strategy. As you ramp, you will assume greater ownership of the analytics layer in our modern data stack. This includes designing, refactoring, and maintaining dbt models that define our core business metrics, improving metric consistency across BI tools, and strengthening the reliability and scalability of reporting. You will collaborate closely with Data Engineering (pipeline and infrastructure) and Data Science (advanced modeling and forecasting), while owning the analytical semantic layer that connects raw data to decision-making. Key Responsibilities Insight & Business Impact - Own growth and lifecycle analytics across paid acquisition, web funnel, and retention. - Analyze Shopify, Amplitude, and marketing channel data to surface actionable insights that influence budget allocation and experimentation strategy. - Lead experimentation analysis (A/B testing, conversion rate optimization, landing page performance). - Develop and maintain executive-ready dashboards and reporting in Looker (with migration to Omni underway). - Partner with stakeholders to define clear success metrics and ensure decisions are grounded in consistent definitions. Analytics Engineering & Data Foundation - Design, refactor, and maintain core data marts in dbt to ensure metric consistency and scalable reporting. - Standardize key business definitions (e.g., CAC, LTV, retention, repeat rate) and enforce alignment across teams. - Improve data quality through QA, reconciliation, and proactive anomaly detection across marketing, commerce, and attribution sources. - Collaborate with Data Engineering on upstream model design and with Data Science on downstream analytical use cases. - Contribute to the evolution of our analytics layer as the company scales. Required Qualifications - 5–8 years of experience in analytics, business intelligence, or a related quantitative role, at least 2+ years supporting eCommerce or DTC businesses. - Advanced SQL proficiency (Snowflake preferred) with the ability to independently build complex queries and analytical datasets. - 3+ years of hands-on experience in growth or marketing analytics, including paid media performance analysis and customer acquisition economics. - Experience working with web or product analytics tools (Amplitude or similar) to analyze funnel and behavioral data. - Demonstrated experience designing and analyzing experiments (A/B testing, CRO). - 2+ years of experience working with dbt or similar transformation frameworks, with practical understanding of dimensional modeling and metric layer design. - Experience building and maintaining dashboards in BI tools (Looker or Omni preferred). - Proven ability to independently manage ambiguous requests and translate them into structured analyses. - Strong written communication skills in a remote, asynchronous environment. - Ability to provide partial overlap with US Eastern Time for key alignment meetings (full-day overlap not required). Preferred Qualifications - 7–10 years of total experience in analytics, including ownership of analytical data models in a modern data stack. - Experience in significantly refactoring or owning analytical data marts. - Familiarity with attribution methodologies (including MTA) and reconciling platform-reported vs modeled data. - Experience with tools such as Fivetran or Airbyte. - Experience partnering cross-functionally in high-growth consumer brands. - Comfort collaborating closely with Data Engineering and Data Science in a shared analytics organization. What Success Looks Like - Within the first 6 months, you are driving high-leverage insights across Growth and Lifecycle while building credibility as a strategic thought partner. - Within 12 months, you have meaningfully improved the structure and reliability of our core data marts, clarified metric definitions across teams, and reduced friction in reporting and decision-making. - This role is suited for someone with sufficient depth to operate independently from day one, while progressively shaping the analytics foundation that supports the business long term.