About the role

Role Overview

Own the design, implementation, and scaling of core data infrastructure and streaming/analytics pipelines that power analytics and machine learning systems.

Responsibilities

Design and build scalable ETL/ELT pipelines using Python, SQL, and Apache Spark to ingest data into a cloud data warehouse
Own data warehouse modeling and architecture using tools such as Snowflake, BigQuery, or dbt, ensuring query performance and data governance
Monitor pipeline health and reliability, with automated alerting and recovery for data drift/latency issues
Collaborate with analytics and engineering to define data contracts, schemas, and metric definitions for business-critical dashboards
Write clean, testable, well-documented infrastructure-as-code and pipeline orchestration code using Airflow or Prefect
Participate in code reviews, improve data engineering best practices, and optimize workflows for cost and performance

Requirements

3–6 years of experience in data engineering / backend / analytics engineering focused on large-scale data systems
Strong SQL and Python skills; experience delivering production-grade data pipelines
Hands-on experience with cloud data warehouses (Snowflake, BigQuery, Redshift) and transformations (dbt)
Familiarity with orchestration tools (Apache Airflow, Prefect, or Dagster) and Docker
Solid understanding of data modeling, including dimensional modeling and data quality frameworks

Bonus

Experience with real-time streaming (Kafka or Flink)
Infrastructure-as-code with Terraform

About Evlo AI

Evlo AI builds AI-driven products and relies on analytics and machine learning systems powered by robust, high-throughput data infrastructure. The role supports the design and scaling of data pipelines and data warehouses used by engineering and product analytics teams.

Data Engineer

Tags