Our interviews focus on your ability to apply technical knowledge to real-world data challenges. Expect a mix of technical deep dives and behavioral questions about your past experiences.

Spark and Big Data

This category tests your understanding of distributed systems and your ability to process large datasets efficiently.

• Explain the difference between a transformation and an action in Spark.
• How do you handle data skew in a Spark join operation?
• Describe the process of migrating a MapReduce job to Spark. What are the primary benefits?
• How does Spark handle fault tolerance?
• What are the advantages of using Parquet as a storage format in a Big Data ecosystem?

SQL and Data Architecture

These questions evaluate your ability to design and query data structures that power business intelligence.

• Write a SQL query to find the second highest salary in a table (or similar ranking logic).
• Explain the concept of micro-partitioning in Snowflake.
• What is the difference between a Star Schema and a Snowflake Schema?
• How do you use Window Functions to perform time-series analysis in SQL?
• Describe a situation where you had to choose between a normalized and a denormalized data model.

Behavioral and Project Experience

We want to understand how you work in a team and how you approach project delivery.

• Tell me about the most challenging data pipeline you have ever built. What made it difficult?
• Describe a time you had to deal with a major data quality issue in production.
• How do you prioritize tasks when you have multiple competing deadlines?
• Give an example of a time you had to explain a technical concept to a non-technical stakeholder.
• Why are you interested in working for TIAA, and how do you align with our mission?

ETL Frameworks and Legacy Systems

While we are modernizing, a deep understanding of ETL (Extract, Transform, Load) principles remains essential. This includes experience with tools like DataStage and the ability to manage data workflows in a Unix environment.

Be ready to go over:

• DataStage Configurations – Understanding grid environments and configuration files.
• Unix Shell Scripting – Basic to intermediate commands for file manipulation and job scheduling.
• Data Integrity – How to ensure data quality and consistency throughout the ETL lifecycle.

Example questions or scenarios:

• "How do you handle error logging and recovery in a multi-stage ETL pipeline?"
• "Explain the purpose of an 'apt' configuration file in a DataStage environment."

Key Responsibilities

As a Data Engineer at TIAA, your primary responsibility is the design, development, and implementation of robust data pipelines. You will be tasked with ingesting data from a variety of sources—ranging from legacy mainframes to modern APIs—and transforming it into actionable datasets for our brokerage and investment teams. This involves not only writing code but also ensuring that all data movement complies with our strict internal governance and external regulatory requirements.

You will collaborate closely with Data Scientists, Business Analysts, and Product Managers to understand their data needs and provide them with high-quality, low-latency data solutions. A typical day might involve optimizing a Snowflake warehouse, debugging a Spark job in a production environment, or participating in architectural reviews for a new data product.

Furthermore, you will play a key role in our ongoing digital transformation. This includes migrating existing MapReduce jobs to Spark, automating manual data processes using Python or Unix scripts, and contributing to the continuous improvement of our data engineering standards and best practices.

Role Requirements & Qualifications

We look for candidates who combine technical depth with a pragmatic approach to engineering. For the Sr. Brokerage Investment Custodial Data Engineer level, we expect a high degree of autonomy and the ability to lead complex projects.

• Technical Skills – Proficiency in Spark (Scala/Python), SQL, and cloud data platforms like Snowflake is essential. Familiarity with DataStage, Hadoop, and Unix commands is highly preferred.

• Experience Level – Typically, candidates should have 5+ years of experience in data engineering or a related field, with a proven track record of delivering scalable data solutions in a complex environment.

• Domain Knowledge – Experience in the financial services sector, particularly in brokerage or investment data, is a significant advantage.

• Soft Skills – Strong communication skills are a must. You should be able to articulate technical concepts to non-technical stakeholders and demonstrate a commitment to data quality and integrity.

• Must-have skills – Spark, SQL, ETL design, Python or Scala.

• Nice-to-have skills – Snowflake certification, experience with AWS/Azure, and knowledge of financial regulatory reporting.

Frequently Asked Questions

Q: How technical is the managerial round? The managerial round at TIAA is usually less about coding and more about your approach to work. Expect questions about your project management style, how you handle conflict within a team, and your long-term career goals. However, don't be surprised if they ask high-level architectural questions to gauge your seniority.

Q: What is the most important skill to highlight during the interview? While technical skills are a baseline, the ability to demonstrate project ownership is what sets successful candidates apart. Be ready to walk through your past projects in great detail, explaining your specific contributions and the impact your work had on the business.

Q: Does TIAA allow for remote or hybrid work for Data Engineers? TIAA generally follows a hybrid work model, though specific requirements can vary by team and location (such as our New York or Pune offices). It is best to clarify the specific expectations for your role during the initial recruiter screen.

Q: How long does the hiring process typically take? The process from the first phone screen to a final offer typically takes between 3 to 6 weeks. We strive to keep candidates updated at every stage, though the final director and HR approvals can sometimes add a few days to the timeline.

Other General Tips

• Master the Fundamentals: Even if you are an expert in Spark, do not neglect your SQL and Unix basics. Many of our core systems still rely on these foundational technologies.
• Be Specific with Examples: When discussing your projects, use the STAR method (Situation, Task, Action, Result). Quantify your results whenever possible (e.g., "reduced processing time by 30%").
• Understand the Domain: Take some time to learn about TIAA's mission and the basics of brokerage and retirement services. Showing that you understand the data you will be working with is highly impressive.
• Ask Thoughtful Questions: Use the time at the end of the interview to ask about the team's tech stack evolution, the biggest data challenges they are currently facing, and how they define success for this role.