Data Engineer

Evlo AI; Austin (TX); On-site; USD 120,000 - 160,000; Full time

Job summary

Evlo AI is seeking a data engineer to own the design, implementation, and scaling of data infrastructure, ETL pipelines, and analytical warehouses supporting core business operations. You will collaborate with data scientists, analytics engineers, and software teams to ensure reliable, secure, and high-performance data flows.

You will design scalable pipelines in Python and SQL, manage Snowflake/BigQuery/Redshift, and implement CI/CD with Terraform, Airflow, and streaming with Kafka/Kinesis.

Qualifications

• 3–6 years of experience in data engineering, building production-grade data pipelines and distributed data systems
• Expert-level SQL and advanced Python programming skills, with a strong foundation in data structures and algorithm design
• Hands-on experience with modern cloud data warehouses (Snowflake, BigQuery, or Redshift) and orchestration tools like Apache Airflow or Prefect
• Familiarity with containerization and orchestration technologies, specifically Docker and Kubernetes in cloud environments
• BS or MS in Computer Science, Data Engineering, or a related quantitative field, or equivalent practical experience
• Bonus: Experience with real-time streaming frameworks, dbt (data build tool), or FinOps practices for cloud data cost optimization

Responsibilities

• Design, build, and optimize scalable data pipelines and ETL processes using Python, SQL, and Apache Spark to ingest millions of events daily
• Architect and maintain cloud data warehousing solutions on Snowflake or BigQuery, enforcing robust data modeling and schema design best practices
• Automate data quality checks, anomaly detection, and schema validation to ensure high reliability across all analytical and operational datasets
• Collaborate with software engineers to integrate event-streaming architectures using Kafka or Kinesis for real-time data ingestion
• Monitor pipeline performance, optimize slow-running queries, and manage cloud infrastructure costs associated with data storage and compute
• Write clean, testable, and well-documented infrastructure-as-code using Terraform and participate in continuous integration workflows

Skills

SQL

Python

Data engineering

Algorithm design

Education

BS/MS in Computer Science or related field

Tools

Snowflake

BigQuery

Redshift

Airflow

Prefect

Kafka

Kinesis

Docker

Kubernetes

Terraform

Job description

About The Role

The role owns the design, implementation, and scaling of data infrastructure, high-throughput ETL pipelines, and analytical data warehouses supporting core business operations.

The role owns the design, implementation, and scaling of data infrastructure, high-throughput ETL pipelines, and analytical data warehouses supporting core business operations. The team works closely with data scientists, analytics engineers, and software engineering teams to ensure reliable, secure, and performant data flows across the organization.

Key Responsibilities

• Design, build, and optimize scalable data pipelines and ETL processes using Python, SQL, and Apache Spark to ingest millions of events daily
• Architect and maintain cloud data warehousing solutions on Snowflake or BigQuery, enforcing robust data modeling and schema design best practices
• Automate data quality checks, anomaly detection, and schema validation to ensure high reliability across all analytical and operational datasets
• Collaborate with software engineers to integrate event-streaming architectures using Kafka or Kinesis for real-time data ingestion
• Monitor pipeline performance, optimize slow-running queries, and manage cloud infrastructure costs associated with data storage and compute
• Write clean, testable, and well-documented infrastructure-as-code using Terraform and participate in continuous integration workflows

What We Are Looking For

• 3–6 years of experience in data engineering, building production-grade data pipelines and distributed data systems
• Expert-level SQL and advanced Python programming skills, with a strong foundation in data structures and algorithm design
• Hands-on experience with modern cloud data warehouses (Snowflake, BigQuery, or Redshift) and orchestration tools like Apache Airflow or Prefect
• Familiarity with containerization and orchestration technologies, specifically Docker and Kubernetes in cloud environments
• BS or MS in Computer Science, Data Engineering, or a related quantitative field, or equivalent practical experience
• Bonus: Experience with real-time streaming frameworks, dbt (data build tool), or FinOps practices for cloud data cost optimization