DataOps Engineer IRestoration of America

The DataOps Engineer I at Restoration of America is a junior-to-mid-level role dedicated to streamlining the data lifecycle and ensuring the seamless delivery of high-quality data. Working closely with senior leadership, you will automate workflows, monitor pipeline health, and maintain robust database environments. This role is ideal for a mission-aligned engineer who is passionate about process optimization, reliability, and technical growth within a modern stack of PostgreSQL, dbt, and Airflow.

• Assist with the performance monitoring and routine maintenance of PostgreSQL databases to ensure high availability and security.

• Automate and monitor the deployment of dbt models and Airflow DAGs.

• Manage tasks through SDLC project management application and document engineering standards to ensure transparent and reliable operations.

• Utilize Git for version control and participate in technical planning sessions and production support rotations.

• Monitor data quality standards and assist in implementing security and access controls across the data platform.

Required Qualifications:

• 1–3 years of hands-on experience in a data-centric role (Data Engineering, Database Administration, or Backend Engineering).

• Solid understanding of PostgreSQL fundamentals, including writing complex queries, understanding schema design, and basic performance tuning (indexing).

• Exposure to modern data transformation tools (dbt) and orchestration (Airflow, Dagster, or Prefect).

• Strong ability to write clean, performant SQL.

• Familiarity with automated testing frameworks (e.g., dbt tests, Great Expectations).

• Proficiency with Git and a solid understanding of branching and pull request workflows.

• Deep commitment to the organization’s mission and values, with a desire to apply technical skills toward these goals.

Preferred Qualifications:

• Bachelor's degree in Computer Science, Data Science, or a related field (or equivalent professional experience).

• Foundational experience with dbt (Core or Cloud) and familiarity with cloud data warehouses.

• Basic proficiency in Python for automating data processing tasks or infrastructure tooling.

• Exposure to cloud platforms (AWS, GCP, or Azure) and an interest in learning Infrastructure-as-Code tools like Terraform.

• Familiarity with monitoring and observability tools such as Datadog, Grafana, or Prometheus.

• Knowledge of or interest in distributed data processing and real-time streaming platforms like Kafka or Kinesis.