Data Engineer

Evlo AI; Minneapolis (MN); On-site; USD 110,000 - 150,000; Full time

Job summary

Evlo AI in Minneapolis seeks a data engineer to own the design, implementation, and scaling of core data infrastructure and pipelines handling petabyte-scale datasets. You’ll work with analytics engineers, product teams, and ML engineers to ensure reliable data availability and high quality across the enterprise.

Responsibilities include building scalable pipelines with Python/SQL and Spark, managing Snowflake data warehouses and S3/Redshift, and applying dbt, Airflow, and IaC practices to

Qualifications

• 3–6 years of experience in data engineering, building production-grade data pipelines and distributed systems.
• Advanced SQL proficiency and strong programming skills in Python for data manipulation and automation.
• Hands-on experience with modern data stack tools: dbt, Snowflake, Apache Airflow, and cloud platforms like AWS or GCP.
• Solid understanding of data warehousing concepts, schema design, performance tuning, and data governance frameworks.
• Bachelor’s degree in Computer Science, Statistics, Engineering, or equivalent practical experience.
• Bonus: Experience with streaming architectures using Kafka, containerization with Docker and Kubernetes, or Infrastructure as Code.

Responsibilities

• Design, build, and optimize scalable data pipelines and ETL processes using Python, SQL, and Apache Spark
• Manage cloud-based data warehouses and data lakes using Snowflake, AWS S3, and Redshift
• Implement data modeling best practices, including dimensional modeling and dbt transformations for analytics readiness
• Monitor data pipeline performance, troubleshoot failures, and optimize query performance to reduce cloud infrastructure costs
• Enforce data governance, data lineage tracking, and automated data quality checks using tools like Great Expectations
• Write clean, version-controlled code in Git, participating in code reviews and infrastructure-as-code deployments via Terraform

Skills

Python

SQL

Data pipelines

Distributed systems

Data governance

Education

Bachelor’s degree in Computer Science, Statistics, Engineering, or equivalent practical experience

Tools

dbt

Snowflake

Apache Airflow

Terraform

AWS

GCP

Kafka

Docker

Kubernetes

Job description

About The Role

The role owns the design, implementation, and scaling of core data infrastructure and batch and streaming pipelines that ingest, transform, and serve petabyte-scale datasets.

You will collaborate closely with analytics engineers, product teams, and machine learning engineers to ensure reliable data availability and high data quality across the entire enterprise.

Key Responsibilities

• Design, build, and optimize scalable data pipelines and ETL processes using Python, SQL, and Apache Spark
• Manage cloud-based data warehouses and data lakes using Snowflake, AWS S3, and Redshift
• Implement data modeling best practices, including dimensional modeling and dbt transformations for analytics readiness
• Monitor data pipeline performance, troubleshoot failures, and optimize query performance to reduce cloud infrastructure costs
• Enforce data governance, data lineage tracking, and automated data quality checks using tools like Great Expectations
• Write clean, version-controlled code in Git, participating in code reviews and infrastructure-as-code deployments via Terraform

What We Are Looking For

• 3–6 years of experience in data engineering, building production‑grade data pipelines and distributed systems
• Advanced SQL proficiency and strong programming skills in Python for data manipulation and automation
• Hands‑on experience with modern data stack tools: dbt, Snowflake, Apache Airflow, and cloud platforms like AWS or GCP
• Solid understanding of data warehousing concepts, schema design, performance tuning, and data governance frameworks
• Bachelor’s degree in Computer Science, Statistics, Engineering, or equivalent practical experience
• Bonus: Experience with streaming architectures using Kafka, containerization with Docker and Kubernetes, or Infrastructure as Code