Job Description

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: Versátil schedule with remote and office options.

Requirements
Data Engineering core experience: At least 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.

Required Skill Profession

Computer Occupations

📌 Senior Data Engineer (Bogotá)
🏢 Agileengine
📍 Bogotá