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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Quantitative Snowflake Developer - **Company:** Everforth CyberCoders - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Query Performance, Airflow, Amazon Web Services, Automation of Tests, Microsoft Azure, Cloud Computing, Code Review, Continuous Integration, Data Architecture, Information Engineering, Extract Transform Load (ETL), Data Transformation, Data Stores, Job Scheduling, Python (Programming Language), NumPy, Cloud Services, Standard Sql, SQL Databases, Data Ingestion, Snowflake, Software Troubleshooting, Caching, Git, Pandas, Containerization, Information Technology, Influxdb, Data Management, Software Version Control, Data Pipelines - **Published:** June 12, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=16e4771a6a1c487a ## About the Role Do you have experience in Technical documentation?, Do you have a Bachelor's degree in statistics?, * Bachelors or Masters degree in Computer Science, Engineering, Mathematics, Statistics, Finance, or a related field. * 5+ years of experience building data platforms and pipelines, with at least 3 years of hands-on experience in Snowflake production environments. * Expert-level SQL skills and proven experience designing complex, performant analytical queries and schemas. * Strong Python development experience for data engineering tasks, including libraries such as Pandas, NumPy, and standard testing frameworks. * Deep experience with dbt for data transformations, models, testing, documentation, and CI/CD workflows. * Demonstrated experience optimizing Snowflake performance (clustering, partitioning, caching, resource monitors) and managing costs. * Experience working with time-series or high-frequency datasets is highly desirable (nice-to-have: familiarity with time-series platforms like KDB, TimescaleDB, InfluxDB, or specialized tick-data stores). * Familiarity with cloud platforms (AWS, GCP, or Azure), containerization, orchestration (Airflow/Prefect), and version control (Git). * Strong communication skills and experience collaborating with quantitative teams; ability to translate business and research requirements into technical solutions. * Leadership experience mentoring engineers, driving standards, and managing delivery of complex projects. ## Description We are seeking a Lead Quantitative Snowflake Developer to design, build, and optimize data platforms that power quantitative analytics and trading research. You will lead the development of scalable Snowflake-based data architectures, implement robust ETL/ELT pipelines using dbt, and write production-quality Python and SQL to support time-series and event-driven datasets. The role combines technical ownership, hands-on engineering, and collaboration with quantitative researchers to deliver reliable, high-performance data products for analytics and modeling., * Lead design and implementation of Snowflake data architectures to support quantitative analytics, ensuring scalability, security, and cost-efficiency. * Develop, maintain, and optimize ELT/ETL pipelines using dbt and SQL to transform raw market, reference, and event data into clean analytical datasets. * Write production-grade Python and SQL for data ingestion, transformation, validation, and orchestration; implement robust testing and monitoring. * Collaborate closely with quantitative researchers, data scientists, and engineers to translate analytical requirements into data models and pipelines. * Optimize query performance, clustering, micro-partitioning, and storage strategies in Snowflake to meet low-latency analytics needs. * Establish data quality, lineage and governance practices, including automated testing, documentation, and CI/CD for dbt projects. * Mentor and lead a small team of data engineers, setting standards for best practices, code reviews, and architectural decisions. * Build and maintain processes for handling large-scale time-series and tick-level data, including partitioning, retention, and compression strategies. * Integrate Snowflake pipelines with orchestration tools and cloud services (e.g., Airflow, Prefect, AWS/GCP/Azure) to enable reliable job scheduling and alerting. * Drive cross-functional initiatives to improve platform reliability, observability, and cost control, and support ad-hoc analysis and performance troubleshooting. ## Related Videos - [How building an industry DBMS differs from building a research one](https://www.wearedevelopers.com/videos/768-how-building-an-industry-dbms-differs-from-building-a-research-one) - [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) - [Vectorize all the things! 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