Data Engineer

Evlo AI; San Francisco (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

Evlo AI in San Francisco seeks a data engineer to own the data infrastructure that powers decision-making across the company. You will design pipelines moving terabytes of data into trusted datasets and ensure the warehouse stays fast, cost-efficient, and correct as volumes grow.

You will collaborate with analytics engineers, product analysts, and backend teams to turn raw data into reliable datasets and enforce data contracts across the warehouse.

Qualifications

• 3–6 years of experience in data/analytics engineering or backend with data exposure.
• Advanced SQL and strong Python.
• Hands-on with batch/streaming data pipelines and modern warehouses.
• Experience with Airflow, Dagster, or Prefect.
• Hands-on with dbt and data modeling best practices.
• Familiarity with Kafka or Kinesis and cloud platforms (AWS, GCP).
• Bachelor's degree in CS, Engineering, or equivalent.

Responsibilities

• Design, build, and maintain batch and streaming data pipelines.
• Develop and optimize dbt models in Snowflake or BigQuery.
• Ingest data from third‑party APIs and streaming services into the warehouse.
• Implement data quality checks and monitoring to catch issues early.
• Collaborate with analysts to model activation, retention, revenue, and growth metrics.
• Optimize warehouse performance, including partitioning and incremental modeling.
• Contribute to platform decisions on orchestration, storage, and tooling; participate in code reviews and on‑call rotations.

Skills

SQL

Python

Airflow

Dagster

Prefect

dbt

Snowflake

BigQuery

Redshift

Kafka

Kinesis

AWS

GCP

Education

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

Tools

Airflow

Dagster

Prefect

dbt

Snowflake

BigQuery

Redshift

Kafka

Kinesis

AWS

GCP

Job description

About The Role

The role owns the data infrastructure that powers decision-making across the company – designing pipelines that move terabytes of event, transactional, and third‑party data into reliable, queryable models.

You will work closely with analytics engineers, product analysts, and backend teams to turn raw data into trusted datasets, and ensure the warehouse stays fast, cost-efficient, and correct as data volume scales.

Key Responsibilities

• Design, build, and maintain batch and streaming data pipelines using Python, SQL, Airflow (or Dagster), and Spark
• Develop and optimize dbt models in Snowflake or BigQuery, enforcing testing, documentation, and data contracts across the warehouse
• Ingest data from third‑party APIs, CDC streams (Fivetran, Debezium, Kafka), and internal services into a well‑modeled lake and warehouse architecture
• Instrument data quality checks and automated monitoring to catch schema drift, freshness issues, and pipeline failures before downstream teams do
• Partner with analysts and analytics engineers to model business‑critical datasets: activation, retention, revenue, and growth metrics
• Optimize warehouse performance and cost, including query tuning, partitioning strategies, and incremental model design
• Contribute to platform decisions on orchestration, storage, and tooling; participate in code reviews and on‑call rotations

What We Are Looking For

• 3–6 years of experience in data engineering, analytics engineering, or backend engineering with heavy data exposure
• Expert‑level SQL and strong Python; deep experience with at least one modern warehouse (Snowflake, BigQuery, Redshift)
• Production experience with workflow orchestration tools such as Airflow, Dagster, or Prefect
• Hands‑on experience with dbt or strong opinions about dimensional modeling and data modeling best practices
• Familiarity with streaming technologies (Kafka, Kinesis) and cloud platforms (AWS, GCP)
• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience
• Bonus: experience with DataHub/Amundsen‑style catalogs, Terraform for data infrastructure, or real‑time analytics (ClickHouse, Druid)