Data Engineer

Evlo AI; Chicago (IL); Hybrid; USD 120,000 - 170,000; Full time

Job summary

Evlo AI seeks a data engineer to own the design, implementation, and scaling of core data infrastructure, building robust ELT pipelines for analytics and ML applications.

The data engineering team works at the intersection of software engineering and analytics, ensuring high-throughput ingestion, transformation, and availability across distributed cloud environments.

Qualifications

• 3–6 years of data engineering or backend development experience.
• Proficiency in SQL, Python, and shell scripting.
• Experience with cloud data warehouses (Snowflake, BigQuery, or Redshift) and distributed data processing frameworks (Spark, dbt).
• Experience with cloud infrastructure (AWS, GCP, or Azure) and infrastructure-as-code tools like Terraform or CloudFormation.
• Bonus: Familiarity with real-time streaming technologies such as Kafka or Flink, and experience contributing to open-source data tools

Responsibilities

• Design and build scalable data pipelines in Python and SQL using modern orchestration tools like Apache Airflow or Prefect to process large-scale streaming and batch data sets
• Develop and maintain high-performance data models within cloud data warehouses such as Snowflake or BigQuery following dimensional modeling best practices
• Implement automated data quality checks, anomaly detection, and lineage tracking to maintain strict reliability and governance standards
• Optimize data storage, query performance, and pipeline execution times to minimize cloud infrastructure costs and maximize throughput
• Collaborate with data analysts and data scientists to operationalize analytical datasets and machine learning features for production use

Skills

SQL

Python

Shell scripting

Data modeling

Tools

Airflow

Prefect

Snowflake

BigQuery

Redshift

Spark

dbt

Terraform

CloudFormation

AWS

GCP

Azure

Kafka

Flink

Job description

About The Role

The role owns the design, implementation, and scaling of the core data infrastructure, building robust ELT pipelines that power enterprise analytics and machine learning applications.

The data engineering team works at the intersection of software engineering and analytics, ensuring high-throughput data ingestion, transformation, and availability across distributed cloud environments.

Key Responsibilities

• Design and build scalable data pipelines in Python and SQL using modern orchestration tools like Apache Airflow or Prefect to process large-scale streaming and batch data sets
• Develop and maintain high-performance data models within cloud data warehouses such as Snowflake or BigQuery following dimensional modeling best practices
• Implement automated data quality checks, anomaly detection, and lineage tracking to maintain strict reliability and governance standards
• Optimize data storage, query performance, and pipeline execution times to minimize cloud infrastructure costs and maximize throughput
• Collaborate with data analysts and data scientists to operationalize analytical datasets and machine learning features for production use

What We Are Looking For

• 3–6 years of professional experience in data engineering or backend development focused on large-scale data systems
• Advanced proficiency in SQL, Python, and shell scripting, with a strong emphasis on writing clean, modular, and testable code
• Hands-on experience with cloud data warehouses (Snowflake, BigQuery, or Redshift) and distributed data processing frameworks (Spark, dbt)
• Experience with cloud infrastructure (AWS, GCP, or Azure) and infrastructure-as-code tools like Terraform or CloudFormation
• Bonus: Familiarity with real-time streaming technologies such as Kafka or Flink, and experience contributing to open-source data tools