> Markdown version of [/jobs/ext/1862350-senior-data-analyst](https://www.wearedevelopers.com/jobs/ext/1862350-senior-data-analyst). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Analyst - **Company:** The Helper Bees - **Location:** Austin, TX, United States (Remote available) - **Experience:** Expert - **Salary:** $125,000.0 - $150,000.0 - **Contract:** Permanent contract - **Skills:** Sql Data Warehouse, Artificial Intelligence, Airflow, BigQuery, VoIP, Customer Data Management, Directed Acyclic Graph (Directed Graphs), Data Visualization, Cursor (Graphical User Interface Elements), Software Debugging, Office Suite, Power BI, Salesforce.Com, SQL Databases, Tableau (Software), Sql Optimization, Snowflake, Git, Information Technology, Data Analytics, Looker Analytics, Software Version Control, Data Pipelines, Workday - **Published:** July 31, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=ff01f29ce0270ed4 ## About the Role * Advanced SQL, including writing, debugging, and optimizing complex queries against a cloud data warehouse. * Hands-on experience with dbt (or a strong, demonstrable ability to ramp quickly) - building models, tests, and documentation. * Strong data visualization skills and experience building internal data products and BI dashboards (experience with any modern BI tool, e.g., Power BI, Looker, Tableau, or similar). * Comfort with version control and a PR-based workflow (Git/GitHub). * Experience with a cloud data warehouse (BigQuery preferred; Snowflake/Redshift/Synapse comparable) and familiarity with pipeline orchestration (e.g., Airflow) a plus. * Ability to translate business and contract logic (e.g., partner billing terms) into correct, maintainable reporting. * Strong analytical instincts - able to form hypotheses, dig into root causes, and turn data into recommendations, not just charts. * Excellent communication skills, with the ability to work directly with non-technical business and operations leaders and to explain and defend the numbers. * Self-directed and rigorous, with a high personal bar for accuracy, timeliness, and clean work. * Familiarity with, or willingness to actively adopt, AI-assisted analytics and development tooling. * Experience handling sensitive data responsibly; familiarity with HIPAA / PHI handling strongly preferred given our healthcare context. * Exposure to Salesforce data / CRM as a source system a plus. * Interest in growing toward predictive/prescriptive analytics (forecasting, segmentation, recommendation) a plus. * Must be 18 years of age or older., * Bachelor's degree in a quantitative or technical field (e.g., Statistics, Mathematics, Computer Science, Information Systems, Economics), or equivalent relevant work experience in lieu of a degree. * 5+ years of experience in data analytics, business intelligence, or a related data discipline, with increasing scope and independence. * Demonstrated experience owning recurring reporting or invoicing deliverables end to end, including the accuracy and timeliness of what you ship. * Hands-on experience modernizing reporting - e.g., migrating manual/legacy SQL into a governed transformation layer such as dbt - preferred. * Experience partnering directly with business stakeholders to deliver analysis that informed a decision. Physical Requirements: * Ability to remain at your designated workstation for the duration of the workday * Constantly operates a computer and other office productivity machinery, such as a phone and Voice over Internet Protocol (VoIP). * The ability to communicate information and ideas so others will understand. Must be able to exchange accurate information in these situations. * The ability to observe details at close range (typically on a computer screen) * This position offers the flexibility of remote work at approved locations within the United States. Candidates must have a reliable internet connection and a designated work environment conducive to professional phone calls and sensitive data. Enjoy the convenience and comfort of working remotely while contributing to our team's success. ## Description As a Senior Data Analyst, you will own the analytics and reporting for a line of our business - starting with our Long-Term Care Insurance (LTCI) products - and be the person our business leaders trust to get the numbers right. You will produce the partner reports and invoices our clients depend on, rebuild that reporting on a modern, governed data stack, and increasingly move from reporting what happened to analyzing why and recommending what to do next., This is a senior individual-contributor role. You will work hands-on in SQL and dbt against our cloud data warehouse and build the internal data products - dashboards, BI tools, and automated reporting - that the business relies on. You will partner directly with business and operations leaders - not through layers - to understand their priorities and deliver analysis that changes decisions. You will be expected to work independently, hold a high bar for accuracy and timeliness, and bring opinions about how the reporting should be built, not just execute tickets., * Own the recurring invoices and partner reporting for your business line (initially our Long-Term Care Insurance products and their partner book), delivering them accurately and on time every close. * Rebuild reporting on dbt - moving logic out of manual/bespoke SQL and BI-tool calculated fields into tested, traceable, version-controlled models - following a dbt-first approach for all new work. * Build and maintain internal data products - dashboards, BI tools, and self-serve reporting - that business partners rely on, and retire legacy reports as you migrate them. * Write and maintain clean, tested SQL against BigQuery, and ship changes through a Git branch ? PR ? review ? merge workflow. * Build and monitor scheduled data pipelines (e.g., Airflow DAGs) so reporting runs without manual intervention, with exceptions surfaced via alerts. * Build a strong mental map from source system to output - Salesforce UI object ? underlying tables ? dbt model ? report - and use it to find and fix the root cause when numbers look wrong. * Reconcile old vs. new outputs during migrations and hold the line on "matches prior period within tolerance" before decommissioning anything. * Know the business cold: understand each partner's contract and billing terms (PMPM, per-assessment, flat and admin fees) well enough to recognize why each number is what it is. * Partner directly with business and operations leaders (e.g., LTCI GM, VP of Ops, product managers) to understand priorities, validate reporting, and reconcile analytics against operational ground truth. * Move up the analytics maturity curve - from descriptive reporting toward predictive and prescriptive analysis - bringing hypotheses (not just dashboards) about what drives conversion, cost, and outcomes, and delivering analysis that changes an operational or product decision. * Use AI tooling (e.g., Cursor, Claude Code) to accelerate your work, and share reusable patterns, prompts, and slash commands with the team. * Create and maintain runbooks so your reporting can run through your PTO without a single point of failure. * Flag and fix - rather than preserve - logic that is wrong or unclear; raise risks early when a live report or invoice may be affected. * Other duties as assigned or necessary to support team and company success. Performance Metrics: * Data Quality & Accuracy: Reports and invoices are correct and reconcile to source; number-of-corrections-after-delivery trends toward zero. * Timeliness & Reliability: Deliverables hit every close on time with zero manual edits to production outputs; scheduled pipelines run without intervention. * Modernization / dbt Migration: Legacy and bespoke reporting is progressively converted to tested, traceable dbt models, with legacy retired once reconciled. * Stakeholder Satisfaction: Business and operations partners trust the numbers and the working relationship; reporting reflects their real priorities. * Analytical Impact: Progress from reporting to analysis - measured by analyses that drove an operational or product change. * AI Adoption: Demonstrable, growing use of AI tooling in day-to-day work, including reusable patterns contributed back to the team. * Resilience / Coverage: Work is documented and covered (runbooks, cross-training) so it survives PTO and is not a single point of failure. ## Related Videos - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [Destigmatizing the Workplace: Building Real Inclusion](https://www.wearedevelopers.com/videos/1492-destigmatizing-the-workplace-building-real-inclusion) - [Getting under the skin: The Social Engineering techniques](https://www.wearedevelopers.com/videos/38-getting-under-the-skin-the-social-engineering-techniques) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Making Data Warehouses fast. 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