The Role
Architect and build scalable ETL/ELT pipelines, optimize Snowflake schemas, integrate APIs and event streams, implement orchestration (Airflow/Prefect/Dagster), enforce data quality and lineage, and apply DataOps practices including CI/CD, Docker, testing, and documentation.
Summary Generated by Built In
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
- Data Pipeline Development: Design, build, and maintain reliable, scalable ETL/ELT workflows that process batch and streaming data from diverse sources.
- Data Warehousing & Modeling: Design efficient, production-ready schemas (normalized and denormalized) in Snowflake to optimize query performance and enable enterprise analytics.
- API & Event Integration: Connect and ingest data from third-party REST APIs, event-driven streams, and batch sources into core data storage platforms.
- Orchestration: Maintain and expand workflow orchestration pipelines using modern tools (Airflow, Prefect, or Dagster).
- Data Quality & Observability: Implement automated testing, validation, lineage tracking, and proactive alerting frameworks to guarantee data accuracy and system uptime.
- DataOps & Engineering Standards: Drive CI/CD best practices, maintain code bases using Git and Docker, and adopt basic Infrastructure-as-Code (IaC) patterns.
- Code Excellence: Apply modern software engineering standards—including design patterns, automated unit/integration testing, and clear documentation—to data repositories.
MUST HAVES
- You must be authorized to work for ANY employer in the US (e.g., Green card holders, TN visa holders, GC EAD, H4 EAD, U4U with EAD), as we are unable to sponsor or take over employment visa sponsorship at this time;
- 4+ years of experience as a Data Engineer.
- Core Python Fundamentals: Demonstrable expertise writing modular, maintainable, and well-tested Python code (OOP/functional patterns, package management, standard testing frameworks).
- Advanced SQL & Modeling: Deep knowledge of complex SQL queries, query optimization, database design principles, and normalization/denormalization patterns.
- Data Warehousing: Solid, hands-on experience building, managing, and optimizing data architectures within Snowflake.
- Workflow Orchestration: Production experience using workflow orchestration engines like Apache Airflow, Prefect, or Dagster.
- Integrations & Ingestion: Hands-on experience working with REST APIs, event-driven architectures, and both batch and streaming pipelines.
- Data Quality & Lineage: Experience building automated data quality checks, data lineage, and alerting mechanisms (e.g., using tools like dbt test, Great Expectations, or similar).
- DevOps / DataOps Practices: Strong skills in version control (Git), containerization (Docker), CI/CD automation, and familiarity with Infrastructure-as-Code basics.
- Upper-intermediate English level.
NICE TO HAVES
- Experience with dbt (data build tool) for data transformations.
- Familiarity with major cloud providers (AWS, GCP, or Azure).
- Exposure to message streaming tech like Apache Kafka or AWS Kinesis.
PERKS AND BENEFITS
- Growth without limits: build your skills through mentorship, internal TechTalks, challenging projects, and a dedicated annual learning budget
- Competitive compensation: get recognition that reflects your skills and impact, with regular performance and compensation reviews
- Flexibility: work 100% remotely with flexible hours that support focus, autonomy, and a healthy work rhythm
- Meaningful, modern projects: build impactful products using modern technologies alongside global teams and leading brands
- Collaborative culture: join a supportive environment with zero micromanagement where ideas are welcomed and contributions are recognized
- Well-being & support: access local well-being programs and people-focused support tailored to your location
Skills Required
- Authorized to work for any US employer (no visa sponsorship)
- 4+ years of experience as a Data Engineer
- Core Python fundamentals: modular, maintainable, well-tested code
- Advanced SQL and data modeling, query optimization
- Hands-on Snowflake data warehousing experience
- Workflow orchestration experience (Airflow, Prefect, or Dagster)
- Integrations and ingestion: REST APIs, event-driven streams, batch and streaming pipelines
- Data quality, lineage, and automated testing (dbt test, Great Expectations, or similar)
- DevOps/DataOps practices: Git, Docker, CI/CD, familiarity with IaC basics
- Upper-intermediate English
- Experience with dbt (data build tool)
- Familiarity with major cloud providers (AWS, GCP, or Azure)
- Exposure to message streaming tech (Apache Kafka or AWS Kinesis)
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The Company
What We Do
AgileEngine is a privately held company established in 2010 that builds dedicated teams of designers and developers. We turn good ideas into awesome software that people actually want to use. Some of the biggest names and the hottest startups around the world chose us to build their tech.
