The main function of the Data Engineer is to develop, evaluate, test and maintain architectures and data solutions within our organization. The typical Data Engineer executes plans, policies, and practices that control, protect, deliver, and enhance the value of the organization’s data assets., Design, build, and maintain scalable ETL/ELT data pipelines using tools (Dataswarm, Spark, etc.) Build and maintain dashboards and reporting tools (Unidash, internal BI platforms) Partner with Data Scientists and Product teams to vet data quality and ensure metric integrity Develop and maintain production-grade Hive/Presto tables with proper documentation and SLAs Monitor pipeline health, troubleshoot data quality issues, and implement alerting Optimize query performance and data models for efficiency at scale Support ad-hoc data requests and enable self-serve analytics for cross-functional partners, Answers should be attached on top of candidates resumes. Please ensure candidates are independently providing responses without the use of Ai. Q1: We’‘ve all tried to solve a problem on our own only to discover that others needed to be involved in the solution. Give me an example of a time when you took full responsibility for solving a problem but later realized that you should have included others in the process.

• How did you go about gathering information or data about the problem?
• How did you know you found the root cause?
• What was the outcome. How did you determine whether you were successful?

Q2: Describe a recent problem that you identified and resolved that had a positive impact on the business.

• What steps did you take in identifying and resolving the problem?
• How did you determine the root cause of the problem?
• What facts, information, or data did you consider?
• What was the impact of your efforts?

Q3: Describe a time when you had to resolve a problem and the solution to the problem was not immediately obvious.

• What was the problem?
• Who did you involve?
• What information, data, tools, or resources did you use?
• What was the outcome?

LI-SA1

Russell Tobin offers eligible employee’s comprehensive healthcare coverage (medical, dental, and vision plans), supplemental coverage (accident insurance, critical illness insurance and hospital indemnity), 401(k)-retirement savings, life & disability insurance, an employee assistance program, legal support, auto, home insurance, pet insurance and employee discounts with preferred vendors.

Equal Employment Opportunity Russell Tobin is an equal opportunity employer. We do not discriminate on the basis of the race, religious creed, color, national origin, ancestry, physical disability, mental disability, reproductive health decision making, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, age, sexual orientation, veteran or military status, or any other characteristic protected by applicable federal, state, or local law.

Fair Chance Employment Russell Tobin is a Fair Chance employer. We consider all qualified applicants, including those with criminal histories, in a manner consistent with applicable state and local Fair Chance laws and ordinances, including, the California Fair Chance Act and all applicable local Fair Chance ordinances.

Accommodations We are committed to providing reasonable accommodations to applicants and employees with disabilities. If you require a reasonable accommodation to participate in the application or interview process, or to perform the essential functions of this role, please contact us.

Only applicable for San Francisco Candidates: Under the San Francisco Lactation in the Workplace Ordinance, we will provide written notice of lactation accommodation rights, and this notice will automatically be given upon hiring, any inquiry of parental leave or lactation accommodation.

Bachelor’’s degree in Computer Science, Engineering, Mathematics, or a related quantitative field 3+ years of experience in data engineering, analytics engineering, or a related role Strong proficiency in SQL (Presto/Hive/SparkSQL) Experience building and maintaining data pipelines (Airflow, Dataswarm, or similar orchestration tools) Experience with Python or similar scripting language Familiarity with data warehousing concepts and dimensional modeling Strong problem-solving skills and attention to data quality, 5+ years of relevant experience in data engineering Experience with large-scale data processing frameworks (Spark, Hadoop) Experience with dashboard/visualization tools (Tableau, Unidash, or similar) Experience with logging infrastructure and instrumentation Familiarity with version control systems (Git, Mercurial) Experience working in a fast-paced, cross-functional environment Prior experience at a large tech company