Data Engineer

Evlo AI; Chicago (IL); On-site; USD 110,000 - 160,000; Full time

Job summary

Evlo AI in Chicago is seeking a data engineer to own the design, implementation, and scaling of core data infrastructure and reliable ingestion pipelines processing terabytes daily.

The data engineering team collaborates closely with analytics, product, and machine learning teams to ensure clean, accessible, and high-performance data systems across the entire organization.

Qualifications

• 3–6 years of professional experience in data engineering, backend development, or analytics engineering.
• Advanced SQL and Python programming skills; hands-on with Airflow, Prefect, or Dagster.
• Experience designing and maintaining cloud data warehouses (Snowflake, BigQuery) and data lakes (S3, GCS).
• Experience with distributed data processing frameworks (Spark, Flink, dbt).
• Bachelor's degree in CS, Software Engineering, Data Science, or related field.
• Bonus: Real-time streaming with Kafka, Flink, or Kinesis; remote‑first work experience.

Responsibilities

• Design, build, and maintain scalable data pipelines in Python and SQL, processing large volumes of streaming and batch data from various sources.
• Architect and optimize cloud-based data warehouses and data lakes using Snowflake, BigQuery, or AWS Redshift.
• Implement data modeling best practices, building reliable dimensional models, data marts, and robust semantic layers.
• Monitor data pipeline performance, troubleshoot failures, and optimize ETL/ELT jobs for cost efficiency and low latency.
• Enforce data quality standards, automated testing, and comprehensive data lineage tracking across all production systems.
• Write clean, version-controlled code using Git, participate in peer code reviews, and contribute to internal data engineering documentation.

Skills

SQL

Python

Airflow

Prefect

Dagster

Data modeling

Data lakes

Snowflake

BigQuery

DBT

Spark

Flink

Git

Education

Bachelor's degree in Computer Science, Software Engineering, Data Science, or related field

Tools

Snowflake

BigQuery

AWS Redshift

S3

GCS

Kinesis

Kafka

Apache Airflow

Prefect

Dagster

Job description

About The Role

The role owns the design, implementation, and scaling of core data infrastructure and reliable ingestion pipelines processing terabytes of information daily.

The data engineering team collaborates closely with analytics, product, and machine learning teams to ensure clean, accessible, and high-performance data systems across the entire organization.

Key Responsibilities

• Design, build, and maintain scalable data pipelines in Python and SQL, processing large volumes of streaming and batch data from various sources
• Architect and optimize cloud-based data warehouses and data lakes using Snowflake, BigQuery, or AWS Redshift
• Implement data modeling best practices, building reliable dimensional models, data marts, and robust semantic layers
• Monitor data pipeline performance, troubleshoot failures, and optimize ETL/ELT jobs for cost efficiency and low latency
• Enforce data quality standards, automated testing, and comprehensive data lineage tracking across all production systems
• Write clean, version-controlled code using Git, participate in peer code reviews, and contribute to internal data engineering documentation

What We Are Looking For

• 3–6 years of professional experience in data engineering, backend development, or analytics engineering
• Advanced SQL and Python programming skills, with hands‑on experience using orchestration tools like Apache Airflow, Prefect, or Dagster
• Proven track record of designing and maintaining cloud data warehouses (Snowflake, BigQuery) and data lakes (AWS S3, Google Cloud Storage)
• Deep understanding of distributed data processing frameworks, such as Spark, Flink, or dbt (data build tool)
• Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related technical field
• Bonus: Experience with real-time streaming architectures using Kafka, Flink, or Kinesis; prior experience working in a remote-first technology environment