Senior Data Engineer for Indeed ID82269

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 design and maintain data pipelines and automation systems that power cloud cost visibility, attribution, and performance insights for a Cloud Economics team. You will build scalable ELT/ETL workflows using Python, SQL, dbt, and Airflow across AWS services including Glue, Athena, and Aurora alongside Snowflake, optimizing for performance, reliability, and cost efficiency. The role partners closely with Engineering and Finance stakeholders to support cost optimization initiatives and usage-based decision-making.

WHAT YOU WILL DO

- Design, build, and maintain the Cloud Economics team's data pipelines and automation systems.

- Build and maintain scalable data pipelines and datasets that power cloud cost visibility, attribution, and performance insights.

- Design and implement data models and ingestion frameworks to support large-scale telemetry and usage data.

- Develop tooling that enables cost-aware decision-making across Product, Engineering, and Finance stakeholders.

- Optimize data systems for performance, reliability, and cost efficiency (query tuning, storage strategies, compute optimization).

- Partner with Engineering and Finance teams to support cost optimization initiatives and usage-based insights.

- Ensure data quality, validation, and auditability across end-to-end data workflows.

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 in Data Engineering roles.

- Strong Data Engineering foundation with proven experience building and maintaining end-to-end ELT/ETL pipelines in cloud environments.

- Strong SQL and Python.

- Experience with orchestration tools such as dbt, Airflow, or Dagster.

- AWS data ecosystem experience, including AWS Glue, Amazon Athena, and Amazon Aurora.

- Experience with Snowflake.

- Strong data modeling and large-scale data processing experience.

- Experience with CI/CD, version control, Infrastructure as Code (Terraform), and REST API integrations.

- Demonstrated ability to optimize data pipelines for performance and cost.

- Strong analytical skills with experience in data validation, auditing, and troubleshooting production pipelines.

- Upper-intermediate English level.

NICE TO HAVES

- Scala experience.

- FinOps Foundation Certification (Practitioner or Engineer).

- Experience with cloud cost optimization, tagging strategies, or cost monitoring.

- Experience with AWS and GCP. Experience with Datadog.

- Background in data platform or shared infrastructure engineering.

PERKS AND BENEFITS

- Professional growth: Accelerate your professional journey with mentorship, TechTalks, and personalized growth roadmaps.

- Competitive compensation: We match your ever-growing skills, talent, and contributions with competitive USD-based compensation and budgets for education, fitness, and team activities.

- A selection of exciting projects: Join projects with modern solutions development and top-tier clients that include Fortune 500 enterprises and leading product brands.

- Flextime: Tailor your schedule for an optimal work-life balance, by having the options of working from home and going to the office – whatever makes you the happiest and most productive.

Requirements
+4 years of experience in Data Engineering roles. Strong Data Engineering foundation with proven experience building and maintaining end-to-end ELT/ETL pipelines in cloud environments. Strong SQL and Python. Experience with orchestration tools such as dbt, Airflow, or Dagster. AWS data ecosystem experience, including AWS Glue, Amazon Athena, and Amazon Aurora. Experience with Snowflake. Strong data modeling and large-scale data processing experience. Experience with CI/CD, version control, Infrastructure as Code (Terraform), and REST API integrations. Demonstrated ability to optimize data pipelines for performance and cost. Strong analytical skills with experience in data validation, auditing, and troubleshooting production pipelines.