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AI Summary
Architect and scale a modern data platform by designing production-grade ETL/ELT pipelines and optimizing Snowflake schemas. Implement DataOps practices, workflow orchestration, and automated testing to ensure data reliability and accessibility.
AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.
WHY JOIN US
If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!
ABOUT THE ROLE
We are looking for a Senior Data Engineer to architect, build, and scale a modern data platform — designing production-grade ETL/ELT pipelines, optimizing Snowflake data warehouse schemas, and establishing robust DataOps practices. You will orchestrate workflows using Airflow, Prefect, or Dagster, implement data quality and lineage frameworks, integrate third-party REST APIs and event-driven sources, and apply software engineering standards including CI/CD, Docker, and automated testing to data repositories. The role prioritizes clean, well-tested Python code and a deep commitment to data reliability and accessibility.
WHAT YOU WILL DO
- Design, build, and maintain reliable, scalable ETL/ELT workflows that process batch and streaming data from diverse sources;
- Design production-ready normalized and denormalized schemas in Snowflake to optimize query performance and support enterprise analytics;
- Connect and ingest data from third-party REST APIs, event-driven streams, and batch sources into core data storage platforms;
- Maintain and expand workflow orchestration pipelines using Airflow, Prefect, or Dagster;
- Implement automated data testing, validation, lineage tracking, and proactive alerting frameworks to ensure data accuracy and system uptime;
- Drive CI/CD best practices, maintain codebases using Git and Docker, and adopt Infrastructure-as-Code patterns;
- Apply modern software engineering standards, including design patterns, automated unit and integration testing, and clear documentation.
MUST HAVES
- 4+ years of experience as a Data Engineer;
- Strong proficiency in Python, including modular, maintainable, and well-tested code;
- Advanced knowledge of SQL, query optimization, database design principles, and normalization/denormalization patterns;
- Hands-on experience with Snowflake;
- Production experience with workflow orchestration tools such as Apache Airflow, Prefect, or Dagster;
- Experience working with REST APIs, event-driven architectures, and batch and streaming pipelines;
- Experience implementing automated data quality checks, data lineage, and alerting mechanisms;
- Strong experience with Git, Docker, CI/CD automation, and Infrastructure-as-Code fundamentals;
- Upper-intermediate English level.
NICE TO HAVES
- Experience with dbt for data transformations;
- Familiarity with major cloud platforms such as AWS, GCP, or Azure;
- Experience with message streaming technologies such as Apache Kafka or AWS Kinesis.
PERKS AND BENEFITS
- Professional growth: Mentorship, TechTalks, and personalized growth roadmaps.
- Competitive compensation: USD-based pay with education, fitness, and team activity budgets.
- Exciting projects: Modern solutions with Fortune 500 and top product companies.
- Flextime: Flexible schedule with remote and office options.
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