Data Engineer

Evlo AI; Chicago (IL); Presencial; USD 110.000 - 150.000; Jornada completa

Descripción de la vacante

Evlo AI is seeking a Data Engineer to own the data infrastructure that powers analytics and product decisions. You will design reliable batch and streaming pipelines moving millions of events daily into a modern cloud warehouse.

You will collaborate with analytics engineers, data scientists, and product teams to model data, enforce quality standards, and shorten the path from raw events to business insights.

Formación

• 3–6 years of data engineering or analytics engineering experience.
• Expert-level SQL and strong Python skills with production-grade pipelines.
• Experience with modern cloud data warehouses and orchestration tools.
• Experience on AWS, GCP, or Azure with storage and compute services.
• Solid data modeling knowledge—dimensional modeling and event-based schemas.
• Bachelor's degree in Computer Science, Engineering, or equivalent.

Responsabilidades

• Design, build, and maintain scalable ETL/ELT pipelines using Python, SQL, and orchestration tools such as Airflow, Dagster, or dbt Cloud.
• Develop and optimize data models in Snowflake, BigQuery, or Databricks, balancing performance and cost.
• Implement data quality checks, anomaly detection, and observability tooling.
• Build streaming and near-real-time ingestion pipelines using Kafka, Kinesis, or similar.
• Partner with analytics engineers to define semantic layers, dimensional models, and data contracts.
• Optimize warehouse costs and pipeline performance through partitioning and tuning.
• Contribute to IaC and CI/CD for data assets using Terraform and dbt projects.

Conocimientos

SQL

Python

Data engineering

ETL pipelines

Data modeling

Educación

Bachelor's degree in Computer Science or Engineering

Herramientas

Snowflake

BigQuery

Databricks

Airflow

Dagster

dbt Cloud

Descripción del empleo

About The Role

The role owns the data infrastructure that powers analytics and product decisions across the organization - building reliable batch and streaming pipelines that move tens of millions of events daily from source systems into a modern cloud warehouse.

About The Role

The role owns the data infrastructure that powers analytics and product decisions across the organization - building reliable batch and streaming pipelines that move tens of millions of events daily from source systems into a modern cloud warehouse.

The Data Engineer will work closely with analytics engineers, data scientists, and product teams to model data, enforce quality standards, and reduce the time between raw event capture and business-ready insight.

Key Responsibilities

• Design, build, and maintain scalable ETL/ELT pipelines using Python, SQL, and orchestration tools such as Airflow, Dagster, or dbt Cloud
• Develop and optimize data models in Snowflake, BigQuery, or Databricks, balancing query performance, storage cost, and maintainability
• Implement data quality checks, anomaly detection, and observability tooling to catch pipeline failures and schema drift before they hit downstream consumers
• Build streaming and near-real-time ingestion pipelines using Kafka, Kinesis, or Flossom-equivalent event streaming technologies
• Partner with analytics engineers to define semantic layers, dimensional models, and documented data contracts for core business entities
• Optimize warehouse costs and pipeline performance through partitioning, clustering, incremental processing, and query tuning
• Contribute to infrastructure-as-code and CI/CD practices for data assets using Terraform, GitHub Actions, and version-controlled dbt projects

What We Are Looking For

• 3-6 years of experience in data engineering, analytics engineering, or backend engineering with significant data infrastructure ownership
• Expert-level SQL and strong Python skills, including writing production-grade, tested pipeline code
• Hands-on experience with a modern cloud data warehouse (Snowflake, BigQuery, or Databricks) and orchestration tooling (Airflow, Dagster, or Prefect)
• Experience with one or more cloud platforms: AWS, GCP, or Azure, including storage (S3, GCS) and compute services
• Solid grasp of data modeling techniques - dimensional modeling, Kimball methodologies, and event-based schemas
• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience
• Bonus: Experience with streaming architectures (Kafka, Flink), data cataloging/lineage tools (dbt docs, Monte Carlo, Amundsen), or having built data platforms from early-stage maturity