Data Engineer

Evlo AI; Austin (TX); On-site; USD 140,000 - 190,000; Full time

Job summary

Evlo AI is seeking an experienced data engineer to own our data pipelines and infrastructure supporting analytics and product decisions at scale. You will work with analytics engineers, backend engineers, and product teams to transform raw data into reliable, well-modeled datasets for downstream teams.

Responsibilities include building batch and streaming pipelines, modeling data in Snowflake or Databricks, ensuring data quality, and enhancing observability with robust testing and monitoring.

Qualifications

• 3-7 years of experience building and operating production data pipelines in a modern data stack environment.
• Advanced SQL and strong Python skills; deep experience with at least one orchestrator (Airflow, Dagster, or Prefect).
• Hands-on expertise with a cloud data warehouse (Snowflake, BigQuery, or Databricks) and dbt for transformation workflows.
• Experience with streaming or event-driven architectures (Kafka, Kinesis, Flink, or Spark Structured Streaming).
• Track record of improving data reliability - testing frameworks, monitoring, and incident-driven improvements.
• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

Responsibilities

• Design, build, and maintain scalable data pipelines using Python, SQL, and Airflow, processing batch and streaming data from sources like PostgreSQL, Kafka, and third-party APIs
• Model and optimize data in Snowflake (or Databricks), including dimensional modeling, incremental processing strategies, and query performance tuning
• Own data quality end-to-end: implement tests, monitoring, and alerting using tools like dbt tests, Great Expectations, or custom validation frameworks
• Build and evolve ELT/ETL architecture alongside analytics engineers, ensuring consistent transformations and clear lineage across the warehouse
• Instrument and improve pipeline observability - SLAs, retry logic, backfill strategies, and incident response for data downtime
• Partner with analysts and product teams to translate reporting and experimentation needs into robust, documented data models
• Contribute to infrastructure decisions: warehouse cost optimization, partitioning strategies, and tooling for the broader data platform

Skills

Advanced SQL

Python

Airflow

Dagster

Prefect

Data modeling

Data quality testing

Education

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

Tools

Snowflake

BigQuery

Databricks

dbt

Kafka

Kinesis

Flink

Spark Structured Streaming

Terraform

Job description

About The Role

The role owns the data pipelines and infrastructure that power analytics and product decisions - batch and streaming ingestion, warehouse modeling, and orchestration at a scale of hundreds of millions of events per day.

The role owns the data pipelines and infrastructure that power analytics and product decisions - batch and streaming ingestion, warehouse modeling, and orchestration at a scale of hundreds of millions of events per day.

You will work closely with analytics engineers, backend engineers, and product teams to turn raw operational data into reliable, well-modeled datasets that downstream teams can trust and build on.

Key Responsibilities

• Design, build, and maintain scalable data pipelines using Python, SQL, and Airflow, processing batch and streaming data from sources like PostgreSQL, Kafka, and third-party APIs
• Model and optimize data in Snowflake (or Databricks), including dimensional modeling, incremental processing strategies, and query performance tuning
• Own data quality end-to-end: implement tests, monitoring, and alerting using tools like dbt tests, Great Expectations, or custom validation frameworks
• Build and evolve ELT/ETL architecture alongside analytics engineers, ensuring consistent transformations and clear lineage across the warehouse
• Instrument and improve pipeline observability - SLAs, retry logic, backfill strategies, and incident response for data downtime
• Partner with analysts and product teams to translate reporting and experimentation needs into robust, documented data models
• Contribute to infrastructure decisions: warehouse cost optimization, partitioning strategies, and tooling for the broader data platform

What We Are Looking For

• 3-7 years of experience building and operating production data pipelines in a modern data stack environment
• Advanced SQL and strong Python skills; deep experience with at least one orchestrator (Airflow, Dagster, or Prefect)
• Hands-on expertise with a cloud data warehouse (Snowflake, BigQuery, or Databricks) and dbt for transformation workflows
• Experience with streaming or event-driven architectures (Kafka, Kinesis, Flink, or Spark Structured Streaming)
• Track record of improving data reliability - testing frameworks, monitoring, and incident-driven improvements
• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience
• Bonus: experience with Terraform/IaC, Spark at scale, reverse ETL tools (Hightouch, Census), or building data platforms from scratch