Data Engineer

Brighton•City of Industry, CA

About The Position

Brighton is a retail accessories brand doing genuinely innovative work in data and analytics. We're looking for a hands-on Data Engineer who is comfortable owning data workflows end-to-end, from ingestion and transformation through deployment, orchestration, monitoring, and production support. This is a small, hands-on data team, so the role extends beyond writing SQL and Python. You'll help maintain the development and runtime environments that our data pipelines depend on and will be expected to troubleshoot issues across the application, orchestration, container, and infrastructure layers. This role reports to the Head of Data & Analytics. The kind of engineer who will do well at Brighton You don't need to be a dedicated DevOps engineer, but you should be comfortable going below the data layer when something breaks. If a pipeline fails because of Python code, you'll investigate the code. If a container won't start, you'll investigate Docker. If an orchestration worker isn't behaving correctly, you'll inspect the worker and its logs. If credentials, networking, dependencies, or the runtime environment are the problem, you're comfortable digging into those areas as well. We're looking for someone who enjoys understanding how the entire data workflow operates and taking ownership of making it reliable.

Requirements

Strong SQL and Python.
Production ETL/ELT pipeline development.
Hands-on experience with a workflow orchestration platform such as Prefect, Airflow, Dagster, Kestra, or similar.
Hands-on Docker/container experience, including building, running, debugging, and maintaining containerized applications.
Experience supporting production data pipelines and troubleshooting failures.
Git and collaborative software development workflows.
Experience with a cloud data warehouse such as Snowflake.
Understanding of data validation, monitoring, logging, retries, idempotency, and production recovery.

Nice To Haves

Experience administering or supporting orchestration workers/agents and their runtime environments.
CI/CD experience for data applications and pipelines.
AWS, Azure, GCP, or DigitalOcean infrastructure experience.
Experience with container registries, environment variables, secrets management, and application configuration.
Linux/server administration and troubleshooting experience.
Infrastructure-as-code experience such as Terraform.
dbt or similar transformation frameworks.
Experience managing development, test, and production environments.

Responsibilities

Build, deploy, and maintain production ETL/ELT pipelines using Python and SQL.
Develop and maintain data models in Snowflake using modern data engineering practices.
Own scheduled and event-driven workflows from source ingestion through downstream delivery.
Develop, deploy, and troubleshoot workflows running through Prefect or similar orchestration platforms such as Airflow or Dagster.
Build and support containerized data applications and pipelines using Docker.
Help maintain reproducible local and production development environments.
Monitor production pipelines and proactively identify failures, stale data, data-quality issues, and infrastructure problems.
Troubleshoot issues across Python applications, containers, orchestration workers, APIs, networking, credentials, and cloud infrastructure.
Implement logging, alerting, retries, validation, testing, and recovery/backfill strategies.
Use Git-based development workflows including branches, pull requests, code review, and controlled production deployments.
Help establish and improve engineering standards around CI/CD, environment management, testing, observability, documentation, and deployment.
Work directly with analysts and business stakeholders to understand requirements and translate business problems into reliable data solutions.