Okay, I've searched for Data Engineering or Data Steward analyst roles in Salt Lake City, UT that are full-time, require at least 1 year of experience, and list SQL and Python as key skills. I have found 137 positions for you.

Sure. Here's the analysis:

Job Analysis:

This Data Engineering - Product Data Engineering role at Goldman Sachs is fundamentally about ensuring the integrity, accessibility, and usability of critical financial data, particularly securities pricing and product reference data, which underpin risk management and trading functions. The role requires a blend of hands-on technical expertise in data modeling, curation, and quality governance along with strong cross-functional collaboration skills to work closely with both business subject matter experts and engineering teams. Given Goldman Sachs’ complex, fast-paced global environment, the candidate will face the challenge of managing vast, multidimensional data sets under strict quality standards, requiring both tactical issue resolution and strategic automation. Success hinges on delivering scalable, high-quality data solutions that directly enable core business functions, demonstrating ownership, commercial impact awareness, and communication finesse. Agile methodologies and an ability to quickly adapt to new tools, such as their open-source Legend platform, are crucial for thriving and innovating within this role.

Company Analysis:

Goldman Sachs is a prestigious and historically significant leader in global investment banking and financial services, known for its rigorous standards and deep expertise. The company’s strong positioning in the financial sector means roles within data engineering carry significant responsibility and influence because they directly support critical business operations and risk controls. The culture is fast-paced, innovation-driven, and demands a high degree of professionalism, ownership, and collaborative problem-solving. Goldman Sachs emphasizes diversity, inclusion, and continuous learning, which fosters an environment where motivated candidates can grow professionally while contributing to impactful work. Within this large and matrixed organization, this role likely serves as a vital linchpin between technical teams and business units, requiring visibility with key stakeholders and strategic alignment with broader firm goals of data-driven risk management and operational excellence. The emphasis on open-source contributions highlights a forward-thinking culture that balances tradition with innovation.

Absolutely. Here are some mock interview questions that could come up:

• Behavioral: Tell me about a time when you identified a data quality issue that had significant business impact. How did you approach resolving it?
• Behavioral: Describe an experience where you had to collaborate with both technical and non-technical stakeholders to design or improve a data solution. What challenges did you face and how did you manage them?
• Behavioral: How do you handle situations where you must challenge existing processes or assumptions to improve data governance or quality?
• Behavioral: Give an example of a time you had to quickly learn and apply a new technology or methodology in your work. How did you ensure effectiveness?
• Technical/Role-Specific: Explain your experience applying domain-driven design principles in data engineering projects. How did it influence your data models?
• Technical/Role-Specific: How would you approach designing a data model for complex financial product data that requires accuracy, scalability, and regulatory compliance?
• Technical/Role-Specific: Which tools and approaches do you use to proactively detect and resolve data quality issues in large datasets?
• Technical/Role-Specific: Have you worked with open-source platforms like Legend or similar data modeling environments? Please describe your experience.
• Situational: Imagine you receive conflicting data input from two major external vendors impacting pricing data. How would you investigate and resolve the discrepancies?
• Situational: Suppose a key business user requests a complex data extraction that does not currently exist. How would you engage with them and deliver a solution while managing their expectations?
• Situational: If you discover a recurring automation failure affecting data curation pipelines, how would you prioritize your response and communicate with stakeholders?
• Company Fit/Motivation: Why are you interested in pursuing a data engineering role at Goldman Sachs, specifically within the Product Data Quality team?
• Company Fit/Motivation: How do you see your personal values and working style aligning with Goldman Sachs’ culture and mission?
• Company Fit/Motivation: Goldman Sachs values innovation alongside tradition. How have you balanced maintaining quality and compliance with driving innovation in your past roles?
• Candidate Questions for Interviewer: How does the Product Data Quality team collaborate with other global offices to ensure consistency, and what are the biggest challenges in cross-location coordination?
• Candidate Questions for Interviewer: What opportunities for professional growth and skill development does Goldman Sachs provide to data engineers in this role?
• Candidate Questions for Interviewer: Can you describe how success is measured for this role in the first 6-12 months, and what immediate priorities the new hire would focus on?