> Markdown version of [/jobs/ext/52327-senior-analytics-engineer](https://www.wearedevelopers.com/jobs/ext/52327-senior-analytics-engineer). 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 Analytics Engineer - **Company:** Insight Global - **Location:** San Jose, CA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Automation of Tests, Big Data, Code Review, Continuous Integration, Information Engineering, Data Governance, Data Systems, Data Warehousing, Distributed Data Store, Feature Engineering, Macros, Sql Optimization, Apache Spark, Information Technology, Databricks - **Published:** May 26, 2026 - **Apply:** https://www.juju.com/job/00000000g2tstv ## About the Role Experience with healthcare RCM data (claims, denials, payments, AR aging). - Familiarity with AR Follow-Up workflows or denial management processes. - Experience supporting automation, workflow optimization, or rule-based systems. - Exposure to ML feature engineering or AI-enabled analytics. - Experience in regulated or compliance-driven environments. - Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or a related field. - 5+ years of experience in analytics engineering, data engineering, or advanced BI roles. - Strong hands-on experience with dbt or similar ELT frameworks. - Experience with distributed data platforms such as Databricks, Spark, or equivalent. - Advanced SQL skills and experience working with large-scale data transformations. - Solid understanding of analytics engineering best practices, including automated testing, CI/CD, and data governance. - Strong communication skills and ability to work cross-functionally. - Interest in supporting AI, ML, or intelligent automation initiatives. ## Description The Analytics Engineer will support data, automation, and AI initiatives focused on Revenue Cycle Management (RCM), with an emphasis on automating the Accounts Receivable (AR) Follow-Up function. This role will design scalable, production-grade data models using dbt in a modern cloud-based lakehouse environment. In addition to core analytics engineering responsibilities, this position will help enable AI-driven workflows such as intelligent account prioritization, denial trend detection, predictive insights, and workflow optimization. The ideal candidate brings strong analytics engineering fundamentals, modern data stack experience, and a systems-oriented mindset. Responsibilities - Design, build, test, and maintain high-quality data models and transformation pipelines using dbt in a distributed data environment. - Develop scalable, reusable, and well-tested data models, macros, and automation logic supporting AR Follow-Up workflows. - Partner with operations, product, and engineering stakeholders to translate business workflows into reliable, automated data solutions. - Establish and follow analytics engineering standards, including modeling conventions, testing practices, and documentation. - Participate in technical design discussions, architecture reviews, and code reviews to ensure data quality and long-term scalability. - Troubleshoot and resolve complex data issues across ingestion, transformation, and reporting layers. - Optimize data tables and processing workloads for performance, scalability, and cost efficiency. - Support datasets enabling account prioritization, denial analysis, aging performance, and workflow automation. ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source) - [Crafting Custom Frameworks with Rust: A Deep Dive into Procedural Macros](https://www.wearedevelopers.com/videos/849-crafting-custom-frameworks-with-rust-a-deep-dive-into-procedural-macros) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [OLTP in the Lakehouse: Redefining Data for AI Workloads](https://www.wearedevelopers.com/videos/2038-oltp-in-the-lakehouse-redefining-data-for-ai-workloads) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk)