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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Analyst - **Company:** CRITICAL PROPULSION LLC - **Location:** Dallas, TX, United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** HTML, Application Programming Interfaces (APIs), Artificial Intelligence, Microsoft Azure, BigQuery, Cloud Database, Data Profiling, Data Visualization, Database Queries, Xbrl, Graph Database, Python (Programming Language), PostgreSQL, Microsoft SQL Server, SQL Azure, Neo4j, PowerDesigner, Power BI, SQL Databases, Tableau (Software), Parquet, Snowflake, Apache Spark, Model Validation, Data Lakes, Avro, Databricks - **Published:** May 13, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=0d24c78be0131037 ## About the Role Do you have experience in Taxonomy?, * Deep experience with data modeling tools like Erwin, PowerDesigner, or equivalent. * Strong SQL skills across relational and cloud databases: SQL Server, PostgreSQL, Snowflake, BigQuery, or Redshift. * Familiarity with Azure cloud data services: Data Lake, Data Factory, Azure SQL. * Experience with graph databases (Neo4j) or multiple storage formats (Parquet, AVRO, Delta). * Hands-on experience with Databricks for data modeling, analysis, and exploration. You've used notebooks, Unity Catalog, and Databricks SQL to build and validate models at scale, not just run ad hoc queries. * Experience using Databricks AI capabilities: Databricks Assistant, AI Functions, or Mosaic AI for accelerating data profiling, anomaly detection, or model validation workflows. * Comfort with spec-driven development. You've written data specs that engineers can build pipelines from in a single pulse cycle without ambiguity. * Data visualization experience with Tableau, Power BI, or similar. * Background in consulting or client-facing delivery. You've worked with stakeholders who care about the data, not just the dashboard. ## Description Critical Propulsion amplifies human delivery. Our swarm model pairs 3-4 senior operators with AI agents to cut cycle times 30-50% and multiply effective capacity 2-5x versus traditional teams. No sprint theater. Week 1 is productive delivery, not onboarding. We define outcomes and trust operators to get there. What we expect * You build data models from scratch. Conceptual, logical, physical. Star schemas, flat schemas, transactional models. You've done this for green-field projects and you understand the trade-offs between normalization and denormalization at scale. * You extract and standardize data from messy, real-world formats: PDF, HTML, XBRL, iXBRL, APIs, flat files. You don't flinch at ugly source data. * You embrace agentic development. You don't treat AI as autocomplete. You delegate real work to AI agents, review their output critically, and iterate fast. You see this as the future of how data work gets done, not a novelty. * You write Python, SQL, and Spark to manipulate, analyze, and automate. You're not just a GUI modeler. You write code to solve data problems. * You define and enforce data modeling standards. You conduct analysis to validate compliance, identify anomalies, and catch quality issues before they compound. * You communicate directly. When the data doesn't support what someone wants to believe, you say so with evidence, not a caveat-filled email. * You operate autonomously. No status meetings that could be a message. No decks that could be a decision. What we don't filter on * Years of experience as a number. If you can model data for a swarm shipping in 5-day pulse cycles, the number on your resume is irrelevant. * Specific tooling as a prerequisite. We care about data modeling judgment and analytical instinct, not which vendor's logo is on your resume. * Pedigree. No school or company name substitutes for demonstrated ability to build data models that hold up in production. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [From event streaming to event sourcing 101](https://www.wearedevelopers.com/videos/91-from-event-streaming-to-event-sourcing-101) - [The Resilience of the World Wide Web](https://www.wearedevelopers.com/videos/1281-the-resilience-of-the-world-wide-web) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Introducing JSON Structure](https://www.wearedevelopers.com/videos/100219-introducing-json-structure) - [NoLoJS - Avoiding JavaScript Cruft with HTML and CSS - Aaron T. 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