Staff Data Engineer

Rocket Mortgage•Los Angeles, CA

About The Position

The Staff Data Engineer is an expert data handler and developer who has demonstrated their capacity for solving problems of broad scope that support the success of their value stream. The Staff Data Engineer provides technical leadership in the design, architecture, and evolution of enterprise data platforms and solutions. They apply data management principles to create robust, scalable, and secure pipelines, while enabling analytical and operational use cases that power business decisions and customer experiences. As a senior-most engineer, they set technical direction, establish standards, and partner with cross-functional stakeholders. They lead critical initiatives, mentor engineers at multiple levels, and influence the long-term data strategy of the organization. They are also comfortable providing technical leadership across distributed and offshore teams, ensuring consistent engineering quality, clear decision-making, and effective execution. Architect and evolve enterprise-scale data pipelines and platforms to support batch, real-time, and event-driven use cases. Collaborate with stakeholders to gather requirements, frame technical tradeoffs, and shape data solutions that align with business priorities and long-term architectural direction. Lead design and development of tabular and dimensional models across multiple business domains. Build and optimize large-scale distributed data processing jobs with a focus on scalability, fault-tolerance, and cost-efficiency. Establish and enforce best practices for data quality, governance, and observability across pipelines and systems. Drive root-cause analysis for complex, systemic data issues and lead efforts to prevent recurrence. Lead and oversee code reviews with a focus on scalability, maintainability, and enterprise-grade quality. Influence and align data architecture strategy across hybrid environments (on-premises, multi-cloud, and SaaS). Define and implement standards for CI/CD, security, monitoring, and incident management for data systems. Lead adoption of modern data engineering practices and tooling, including workflow orchestration, automated testing, CI/CD, data quality controls, and observability. Anticipate upstream and downstream impacts and design solutions to minimize risk and breakages in interconnected pipelines. Monitor and improve performance of large-scale data environments, including cloud cost optimization. Serve as technical escalation point for the most critical data incidents and participate in on-call rotations. Provide on call support in standard rotation with other team members, to identify priority incidents Mentor and coach engineers across teams, developing talent and building a culture of engineering excellence. Provide technical leadership to distributed and offshore engineering teams through architecture guidance, code reviews, operational rigor, and clear communication across time zones. Collaborate with leadership to identify and prioritize technical debt, architectural improvements, and process enhancements. Evaluate and introduce new technologies by staying up to date on industry trends and assessing organizational fit. Represent data engineering in cross-team forums, presenting strategy, challenges, and innovations to both technical and business stakeholders.

Requirements

9 years of experience or equivalent competency building data solutions, including relational and non-relational databases, and supporting implementation and maintenance of batch and real-time data pipelines using cloud services
7 years of experience or equivalent competency in dimensional or tabular data modeling, including leading design reviews.
Deep expertise designing and architecting complex data transformation pipelines across multiple domains and systems, and guiding technical decisions in partnership with stakeholders to align solutions with business goals.
Experience with CI/CD, automated deployments, and DevOps best practices at scale.
Expertise writing and optimizing complex SQL queries in high-volume environments.
Expertise performance tuning queries and debugging and troubleshooting large-scale distributed data processing jobs (ETL, ELT, streaming).
7 years of experience in Python development, including designing, deploying, and maintaining serverless/cloud-native services such as AWS Lambda.
Experience building pipelines to integrate with APIs
Experience orchestrating complex data workflows with platforms such as Airflow, Dagster, Prefect, or similar workflow orchestration tools.
Knowledge of Linux system administration, shell scripting, and networking at scale.
Hands-on experience with PowerBI or similar reporting tools
Experience with cloud data warehouses – preferably Snowflake

Nice To Haves

Bachelor’s or master’s degree in computer science, information technology, or a related field (or equivalent competency).
Experience defining disaster recovery and backup policies for enterprise-scale data platforms.
Proven track record of mentoring, growing, and technically leading engineers across multiple teams.
Experience influencing data engineering strategy, architecture, and standards across an organization.
Experience evaluating and integrating emerging data technologies into production environments.
Experience working with and technically leading offshore engineering teams, including setting expectations, reviewing design and implementation quality, and driving strong cross-time-zone collaboration.

Responsibilities

Architect and evolve enterprise-scale data pipelines and platforms to support batch, real-time, and event-driven use cases.
Collaborate with stakeholders to gather requirements, frame technical tradeoffs, and shape data solutions that align with business priorities and long-term architectural direction.
Lead design and development of tabular and dimensional models across multiple business domains.
Build and optimize large-scale distributed data processing jobs with a focus on scalability, fault-tolerance, and cost-efficiency.
Establish and enforce best practices for data quality, governance, and observability across pipelines and systems.
Drive root-cause analysis for complex, systemic data issues and lead efforts to prevent recurrence.
Lead and oversee code reviews with a focus on scalability, maintainability, and enterprise-grade quality.
Influence and align data architecture strategy across hybrid environments (on-premises, multi-cloud, and SaaS).
Define and implement standards for CI/CD, security, monitoring, and incident management for data systems.
Lead adoption of modern data engineering practices and tooling, including workflow orchestration, automated testing, CI/CD, data quality controls, and observability.
Anticipate upstream and downstream impacts and design solutions to minimize risk and breakages in interconnected pipelines.
Monitor and improve performance of large-scale data environments, including cloud cost optimization.
Serve as technical escalation point for the most critical data incidents and participate in on-call rotations.
Provide on call support in standard rotation with other team members, to identify priority incidents
Mentor and coach engineers across teams, developing talent and building a culture of engineering excellence.
Provide technical leadership to distributed and offshore engineering teams through architecture guidance, code reviews, operational rigor, and clear communication across time zones.
Collaborate with leadership to identify and prioritize technical debt, architectural improvements, and process enhancements.
Evaluate and introduce new technologies by staying up to date on industry trends and assessing organizational fit.
Represent data engineering in cross-team forums, presenting strategy, challenges, and innovations to both technical and business stakeholders.