This role involves a blend of engineering and analytical responsibilities. You will develop and maintain data infrastructure while also analyzing cost data, identifying trends, and working closely with teams to optimize cloud spend. With a heavy focus on AWS, this position requires a strong understanding of AWS services and cost structures. You will leverage tools like Python, Databricks, Snowflake, Airflow, and Looker to deliver impactful insights and solutions that drive cost optimizations and decision-making.

#LI-DNI

Responsibilities

Design, build, and maintain scalable ETL pipelines to process and transform large volumes of cloud cost and usage data
Integrate data from multiple sources, including AWS, into centralized data lakes or warehouses like Snowflake
Develop and maintain data models to support cost analysis and reporting needs
Optimize query performance and storage efficiency for large-scale datasets
Automate recurring data processing tasks and implement robust monitoring for data pipelines
Ensure data accuracy and reliability through validation processes
Analyze cloud cost data to identify trends, anomalies, and optimization opportunities
Work closely with teams to investigate spending changes and resolve cost anomalies
Collaborate with stakeholders to understand cost drivers and provide actionable insights
Support teams in building dashboards and visualizations to track key cost metrics
Create reports and presentations to communicate findings and recommendations to leadership
Partner with teams to develop strategies for cost reduction and operational efficiency

Requirements

Bachelor’s degree in Computer Science, Data Engineering, Data Analytics, or a related field
3+ years of experience in a data engineering, data analysis, or hybrid role
Knowledge of AWS services (e.g., EC2, S3, RDS, Lambda) and their cost structures
Proficiency in SQL and experience with relational databases like Snowflake or Redshift
Familiarity with Databricks and Spark for large-scale data processing
Hands-on experience with ETL tools and frameworks (e.g., Databricks, Apache Airflow)
Programming experience in Python for data analysis and pipeline development
Experience with BI tools like Looker or Tableau for creating dashboards and visualizations
Excellent problem-solving skills and the ability to interpret and analyze large datasets

Nice to have

Experience with Kubernetes, Docker, or containerization technologies
Understanding of cloud cost management tools and strategies
Strong communication skills for presenting insights to stakeholders and leadership