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.

Job Title: Financial Analyst / Data Analyst Position Type: Full-Time, Remote Working Hours: U.S. client business hours (with flexibility for reporting deadlines and project cycles) About the Role: Our client is seeking a Financial Analyst / Data Analyst to build models, analyze performance data, and deliver insights that guide strategy and decision-making. This role requires strong analytical skills, financial acumen, and proficiency with modern data tools. The Analyst serves as a bridge between raw numbers and executive decisions, ensuring reporting is accurate, timely, and actionable. Responsibilities: Financial Modeling: - Build and maintain 3-statement models (P&L, balance sheet, cash flow). - Create scenario and sensitivity analyses to evaluate risks and opportunities. - Model ROI, IRR, break-even, and valuation scenarios for projects or investments. Data Analysis: - Query SQL databases and work with large datasets. - Clean and transform data using Python, R, or Excel advanced functions. - Conduct variance analyses to compare actuals vs. budgets/forecasts. Reporting & Dashboards: - Prepare monthly management reporting packages and board decks. - Build KPI dashboards using Tableau, Power BI, or Looker. - Ensure consistent reporting definitions across finance and operations. Forecasting & Budget Support: - Collaborate with FP&A teams to refine budgets and forecasts. - Incorporate real-time business performance into rolling forecasts. Data Quality & Governance: - Validate data sources for accuracy and consistency. - Document methodologies for transparency and repeatability. Collaboration: - Partner with finance, sales, operations, and leadership to align metrics with goals. - Translate data into clear, actionable insights for non-technical stakeholders. What Makes You a Perfect Fit: - Analytical thinker who can turn complex data into simple insights. - Detail-oriented, with high standards for accuracy. - Strong communicator — equally comfortable with spreadsheets and executive presentations. - Proactive in identifying trends, risks, and improvement opportunities. Required Experience & Skills (Minimum): - 2+ years in financial analysis, FP&A, or data analytics. - Advanced Excel/Google Sheets (pivot tables, INDEX/MATCH, macros). - Proficiency in SQL for querying and joining datasets. - Experience preparing variance analyses and management reports. Ideal Experience & Skills: - Python or R for advanced analytics and data modeling. - Experience with BI tools (Tableau, Power BI, Looker). - Industry background in SaaS, finance, healthcare, or professional services. - Familiarity with ERP systems (NetSuite, SAP, Oracle) for data extraction. What Does a Typical Day Look Like? A Financial Analyst / Data Analyst’s day revolves around turning raw financial and operational data into meaningful insights. You will: - Pull and clean data from ERP or SQL sources to prepare daily/weekly reports. - Update financial models with the latest actuals and run scenario analyses. - Prepare variance analyses to explain deviations from budget or forecast. - Build dashboards in BI tools to give leadership real-time visibility into KPIs. - Collaborate with stakeholders, presenting findings in clear, actionable terms. - Document assumptions and methodologies so models and analyses are transparent and repeatable. In essence: you ensure decision-makers always have accurate, data-driven insights to guide strategy. Key Metrics for Success (KPIs): - Accuracy of forecasts and financial models (variance within ±5–10%). - Timeliness of monthly/quarterly reporting. - Reliability and clarity of dashboards delivered to stakeholders. - Positive feedback from leadership on insights and recommendations. - Reduced errors and improved data quality across reports. Interview Process: - Initial Phone Screen - Video Interview with Pavago Recruiter - Practical Task (e.g., build a simple financial model or create a sample dashboard from dataset) - Client Interview with Finance/Operations Leadership - Offer & Background Verification

• Analyze large-scale Excel datasets to assess structure, quality, and duplicate patterns • Design and develop scalable Power BI data models to support complex analytics • Implement transformation logic to prepare and structure data for reporting • Develop interactive Power BI dashboards to identify duplicate encounters and trends • Create visualizations for encounter groupings, frequency distributions, and severity indicators • Build dynamic filters (facility, date range, encounter type, risk level) to enhance usability • Conduct requirements sessions with HIM, data governance, and operational teams • Translate business needs into technical dashboard specifications and wireframes • Iterate on dashboard design based on stakeholder feedback • Implement automated data refresh processes within Power BI • Establish validation logic to ensure data accuracy and consistency • Produce requirements documentation, data dictionaries, and process documentation • Deliver knowledge-transfer sessions to internal staff to ensure sustainability • Execute deliverables aligned to project milestones and timeline expectations • Support rapid development cycles to meet tight deadlines

• Perform data mapping activities, translating client data into defined global and consumer data standards according to Cotiviti requirements. • Collaborate with internal stakeholders to analyze, understand, and document data requirements and transformation rules. • Explore large and complex datasets to identify trends, patterns, and anomalies. • Quickly pivot and adapt to evolving business needs, delivering insights and solutions in a fast-paced environment. • Create and maintain clear, detailed data mapping documentation to support ongoing and future projects. • Communicate findings, challenges, and recommendations effectively to both technical and non-technical team members. • Host and facilitate discussions with Healthcare data carriers to manage data intake and ensure all client data requirements are discussed and confirmed. • Document meeting discussions and obtain sign-off from the client and carriers. • Perform data quality checks verifying that data provided is consistent with the requirements to build Healthcare Data Warehouse. • Write data transformation specifications to load source data into target data warehouse tables. • Work with the database programmers to perform quality checks on the reformatted coding; ensure data is complete while making modifications as necessary to transform the raw data into table layouts. • Perform data analysis, data mining and identify root cause of issues using SQL. • Assist the Engineering/Tools team with new business requirements and help them with Functional Specifications. • Assist in the conversion of internal processes from manual to automated through coding and programming.