Okay, I've searched for Data Engineer roles in New York, NY within the financial industry, for full-time positions requiring 6 years+ experience and strong SQL skills. I have found 57 positions for you.

Sure. Here's the analysis:

Job Analysis:

This Data Engineer role is fundamentally about bridging complex data ecosystems within the financial domain, particularly focusing on wholesale loans, lending, capital markets, or risk reporting. The candidate is expected to serve as a pivotal technical expert, responsible for consolidating disparate data sources, validating solutions, and ensuring data completeness and quality throughout the data lifecycle. The emphasis on Big Data technologies like Spark, Hive, and Java, combined with experience in Cloudera Data Platform (CDP), reflects a need for hands-on expertise in scalable, distributed data processing frameworks. The role's requirement to produce clear documentation, such as functional specifications and process flow diagrams, highlights the necessity of translating business requirements into actionable technical solutions, indicating that cross-functional communication and stakeholder engagement are crucial. The candidate must be comfortable addressing data lineage and tracing challenges, which suggests a focus on data governance, compliance, and auditability, particularly relevant in regulated financial environments. Success in this role would mean delivering robust data architectures that not only meet current analytics needs but also enable accurate, timely, and trustworthy data for decision-making, while supporting agile delivery and production stability.

Company Analysis:

Dice operates as a specialized tech career platform, connecting tech talent with employers and providing resources tailored to evolving technology professionals. While Dice itself is not the direct employer here, the client — Photon — is embedded within a tech-forward context facilitated by Dice’s marketplace and community. Dice’s positioning as a trusted intermediary suggests a culture that values continuous learning, innovation in job searching, and responsiveness to tech trends — qualities mirrored in the high standards expected of candidates they attract and place. The environment supporting this Data Engineer will likely balance the fast-moving nature of tech hiring with the exacting demands of the financial sector, implying that while agility and adaptability are necessary, there is also an underlying demand for precision, compliance, and reliability. This duality influences the role by requiring a professional who can thrive both in a dynamic, evolving technical landscape and within the strict governance frameworks typical of finance-related data projects. Visibility into cross-team collaboration and adherence to business needs will be key, as this role sits at the intersection of business intelligence consumers and core technology delivery teams, aligning closely with Photon’s strategic importance in financial services.

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

• Tell me about a time when you had to consolidate disparate data sources into a coherent and reliable data pipeline. How did you approach this challenge?
• Describe an instance where you identified and resolved a data quality issue in a production environment. What steps did you take and what was the outcome?
• How have you managed stakeholder requirements that were ambiguous or conflicting when designing data solutions?
• Explain how your expertise with Big Data tools like Spark and Hive has enabled you to optimize data processing workflows.
• Walk me through your experience with data lineage and tracing efforts. Why is this important, and how have you implemented it in past projects?
• Given a scenario where the business users report inconsistencies in loan data reports, how would you investigate and rectify the issue?
• Imagine that you need to coordinate with multiple technology leads to bridge data completeness gaps quickly. How would you prioritize and communicate the necessary steps?
• What strategies do you use to ensure that your data models align with both logical business requirements and physical database constraints?
• Why are you interested in working on data engineering projects specifically within the financial services industry, especially in wholesale loans or capital markets?
• How do you see Dice’s role as a tech career platform aligning with your own career growth objectives?
• Based on what you know about Photon and Dice, how would you describe the ideal culture or environment to help you succeed in this role?
• What are your long-term professional goals, and how do you think this Data Engineer role fits into your career path?
• Can you describe a time when you had to balance speed and accuracy when deploying data solutions, especially under pressure in a financial context?
• How comfortable are you with creating detailed documentation such as functional specs and process flow diagrams? Could you provide examples of your work?
• What questions do you have about Photon’s data infrastructure or Dice’s partnership model that would help you better understand how to contribute effectively?