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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Analytics Engineer (AI Insurance SaaS) - **Company:** EvolutionIQ - **Location:** New York, NY, United States - **Experience:** Expert - **Salary:** $200,000.0 - $225,000.0 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Data Analysis, Computing Platforms, BigQuery, Software as a Service, Cloud Database, Cloud Storage, Databases, Data Architecture, Data Cleansing, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Security, Data Warehousing, Dimensional Modeling, Python (Programming Language), SQL Databases, Data Ingestion, Snowflake, Information Technology, Star Schema, Data Management, Data Pipelines, Amazon Redshift, Programming Languages - **Published:** July 23, 2026 - **Apply:** https://job-boards.greenhouse.io/evolutioniq/jobs/6122675004 ## About the Role * Bachelor's or Master's degree in Computer Science, Engineering, or a related field. * 5+ years of experience in data engineering, with a focus on building analytical data platforms. * Demonstrated experience with cloud data warehousing solutions (e.g. BigQuery, Snowflake, Redshift). * Strong proficiency in SQL and experience with data modeling techniques (e.g., star schema, dimensional modeling). * Experience building and maintaining ETL/ELT pipelines using tools like Dagster, Apache Airflow, dbt, or similar. * Experience with programming languages such as Python, Java, or Scala. * Experience with data governance and data quality tools and processes is highly desirable. * Excellent problem-solving and analytical skills, along with passion for data and a commitment to data quality. ## Description Platform Design and Development: * Lead the design, development, and implementation of a scalable, reliable, and efficient analytical data platform. * Influence decisions on technology and tool selection for data ingestion, storage, transformation, and analysis for the analytics platform. * Contribute to the overall data architecture and strategy, ensuring alignment with business needs and best practices. Data Pipeline Engineering: * Build and maintain robust ETL/ELT pipelines to ingest, transform, and load data from various sources into the data warehouse (e.g., cloud storage, databases, APIs). * Develop and optimize data models for analytical use cases, ensuring data quality, consistency, and accessibility. Data Quality and Governance: * Establish and enforce data quality standards and processes to ensure data accuracy and integrity. * Implement data governance policies and procedures to manage data access, security, and compliance for analytics use cases * Proactively identify and address data quality issues, working with stakeholders to resolve root causes. Collaboration and Communication: * Partner with data scientists, analysts, and other engineers to understand their data needs and provide solutions. * Effectively communicate technical concepts and designs to both technical and non-technical audiences. * Mentor and guide data engineers, fostering a culture of learning and collaboration. Innovation and Continuous Improvement: * Stay up-to-date on the latest trends and technologies in data engineering and analytics. * Identify opportunities to improve existing data processes and tools. * Proactively propose and implement innovative solutions to address data challenges. ## 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) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [Data Governance in the Era of AI](https://www.wearedevelopers.com/videos/1622-data-governance-in-the-era-of-ai) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [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) - [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) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk)