Data Engineer

Evlo AI; Dallas (TX); On-site; USD 110,000 - 170,000; Full time

Job summary

Evlo AI is seeking a data engineer to own the data infrastructure powering analytics and product decisions. You will design batch and streaming pipelines, model data warehouses, and maintain trustworthy data at scale.

Collaborate with analytics engineers, data scientists, and backend teams to convert raw streams and operational data into clean datasets. You will build scalable ETL/ELT pipelines, optimize Snowflake/BigQuery/Redshift layers, and implement data quality checks.

Qualifications

• Must have 3–6 years of data/analytics/backend engineering with heavy data focus.
• Proficient in production-grade, tested pipeline code using SQL and Python.
• Hands-on experience with a modern data warehouse and an orchestrator.

Responsibilities

• Design, build, and maintain scalable ETL/ELT pipelines using Python, SQL, and Airflow/Dagster.
• Model and optimize data warehouse layers (Snowflake/BigQuery/Redshift).
• Build and operate streaming pipelines with Kafka/Kinesis.
• Define semantic layers, dbt models, and data contracts across teams.
• Implement data quality and observability checks with Great Expectations or dbt tests.
• Own IaC for data tooling (Terraform, CI/CD) across cloud environments.
• Document pipelines, runbooks, and data lineage; participate in on-call rotation.

Skills

Advanced SQL

Python

Data engineering

Collaboration

Education

Bachelor's degree in CS/Engineering

Tools

Airflow

Dagster

Prefect

dbt

Snowflake

BigQuery

Redshift

Kafka

Kinesis

Terraform

Job description

About The Role

The role owns the data infrastructure that powers analytics and product decisions - batch and streaming pipelines, warehouse modeling, and the tooling that keeps data trustworthy at scale.

You will work closely with analytics engineers, data scientists, and backend teams to turn raw event streams and operational databases into clean, reliable datasets that everyone downstream can build on.

Key Responsibilities

• Design, build, and maintain scalable ETL/ELT pipelines using Python, SQL, and orchestration tools such as Airflow or Dagster
• Model and optimize data warehouse layers (Snowflake, BigQuery, or Redshift) - dimensional models, incremental loads, and cost-efficient query patterns
• Build and operate streaming pipelines with Kafka, Kinesis, or Fizz-style frameworks to process events in near real time
• Partner with analytics engineers and analysts to define semantic layers, dbt models, and data contracts that standardize metrics across teams
• Implement data quality and observability checks using tools like Great Expectations or dbt tests, with automated alerting on failures and anomalies
• Own infrastructure-as-code for data tooling (Terraform, CI/CD) across AWS, GCP, or Azure environments
• Document pipelines, runbooks, and data lineage; participate in design reviews and on-call rotation for critical data assets

What We Are Looking For

• 3-6 years of experience in data engineering, analytics engineering, or backend engineering with heavy data focus
• Advanced SQL and strong Python skills; experience writing production-grade, tested pipeline code
• Hands-on experience with at least one modern data warehouse (Snowflake, BigQuery, Redshift) and one orchestrator (Airflow, Dagster, Prefect)
• Practical experience with dbt or similar transformation frameworks and dimensional/incremental modeling patterns
• Experience with streaming or event-driven architectures (Kafka, Kinesis) and cloud data services (S3/GCS, Lambda, Spark)
• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience
• Bonus: experience with data cataloging/lineage tools (Amundsen, OpenLineage), Airflow-in-Kubernetes deployments, or building metrics layers used by BI tools like Looker or Tableau