Staff DevOps Engineer, Data

GoodLeap•Plano, TX

46d•$173,000 - $200,000

About The Position

GoodLeap is a technology company delivering best-in-class financing and software products for sustainable solutions, from solar panels and batteries to energy-efficient HVAC, heat pumps, roofing, windows, and more. Over 1 million homeowners have benefited from our simple, fast, and frictionless technology that makes the adoption of these products more affordable, accessible, and easier to understand. Thousands of professionals deploying home efficiency and solar solutions rely on GoodLeap's proprietary, AI-powered applications and developer tools to drive more transparent customer communication, deeper business intelligence, and streamlined payment and operations. Our platform has led to more than $30 billion in financing for sustainable solutions since 2018. GoodLeap is also proud to support our award-winning nonprofit, GivePower, which is building and deploying life-saving water and clean electricity systems, changing the lives of more than 1.6 million people across Africa, Asia, and South America. Position Summary The Staff DataOps Engineer role applies DevOps principles to data platforms, ensuring that pipelines and systems are automated, observable, and reliable at scale. In this role, the engineer partners closely with data engineering, analytics, and infrastructure teams to enable consistent, secure, and performant delivery of data. The Staff DataOps Engineer is a leader who sets strategy for DataOps, designs platform-wide automation and observability patterns, and unblocks teams on their hardest data reliability challenges.

Requirements

Set DataOps strategy: Define and evangelize CI/CD, observability, and testing frameworks adopted by a majority of data services and pipelines.
Architect at scale: Design resilient, cost-efficient data platform architectures across cloud and on-prem environments (batch and streaming).
Standardize the platform: Author Terraform modules, orchestration templates (Airflow/dbt/Dagster), and hardened container images for data workloads.
Advance observability: Lead adoption of data monitoring and lineage tools that cut data incidents and MTTR by double digits.
Drive incident excellence: Act as incident commander for major data outages, run blameless postmortems, and eliminate systemic pipeline toil.
Mentor & influence: Coach senior engineers, run DataOps workshops, and contribute to role definition and career-ladder standards.
Cross-team collaboration: Partner with Security, Compliance, and Operations to balance reliability, cost, and speed in the same manner that new product features are considered.
Infrastructure and Deployment: Strong hands on experience with infrastructure as code (IaC) such as Terraform and automated deployments such as GitHub Actions.

Responsibilities

Define and drive adoption of CI/CD frameworks, infrastructure-as-code, and automation patterns for data platforms. Strong communication, influence, and empathy are critical.
Lead multi-quarter initiatives that materially improve the reliability, scalability, or cost efficiency of data systems. Requires composure under pressure, clear decision-making, and the ability to unite cross-functional teams.
Establish and maintain data observability standards (freshness, accuracy, completeness, lineage). Partner with product and data teams to define and track SLIs/SLOs for critical pipelines.
Mentor senior and mid-level DevOps and Data Engineers, fostering a culture of automation, reliability, and continual learning.
Represent DataOps in executive and stakeholder reviews, translating data reliability risks into business impact to support prioritization and decision-making. Strong written communication and clarity are essential.

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