What is a Data Engineer at Alliant Credit Union?

As a fully digital financial institution with no physical branches, Alliant Credit Union relies entirely on its technology and data infrastructure to deliver exceptional member experiences. In the role of Data Engineer, specifically operating as a Senior Data Quality Developer, you are the primary gatekeeper for the accuracy, reliability, and security of the credit union's enterprise data. Your work directly impacts everything from real-time transaction processing and fraud detection to regulatory reporting and personalized financial products.

This position is critical because financial data leaves no room for error. You will be tasked with designing, developing, and maintaining robust data quality frameworks that monitor the health of massive datasets. You will collaborate closely with data stewards, analytics teams, and software engineers to ensure that data anomalies are caught and resolved before they impact business decisions or member trust. The scale and complexity of the financial data ecosystem at Alliant Credit Union make this role highly strategic and technically engaging.

Expect a highly collaborative environment where your technical solutions carry immediate, measurable business value. You will be working with modern data stacks to build automated validation pipelines, establish governance protocols, and drive a culture of data excellence. If you are passionate about building bulletproof data systems and thrive in an environment where precision is paramount, this role offers a profound opportunity to shape the data foundation of an industry-leading digital credit union.

Getting Ready for Your Interviews

Preparing for the Data Engineer interview requires a balanced focus on core technical competencies and an understanding of the financial domain's strict operational standards. You should approach your preparation by thinking holistically about the lifecycle of data and the systems required to keep it pristine.

Your interviewers will evaluate you against several core criteria:

• Technical Expertise and Data Quality – You must demonstrate deep proficiency in SQL, scripting languages like Python, and modern ETL/ELT frameworks. Interviewers will look for your ability to design automated data validation routines, anomaly detection systems, and robust data models.
• Problem-Solving and Troubleshooting – Financial data pipelines often break in unexpected ways. You will be evaluated on your logical approach to diagnosing data discrepancies, tracing lineage, and implementing permanent architectural fixes rather than temporary patches.
• Domain Awareness and Risk Mitigation – Working at Alliant Credit Union requires an appreciation for data governance, compliance, and security. You can show strength here by incorporating edge cases, privacy considerations, and regulatory constraints into your technical designs.
• Collaboration and Communication – As a Senior Data Quality Developer, you will bridge the gap between technical teams and business stakeholders. Interviewers will assess your ability to explain complex data issues to non-technical audiences and influence cross-functional teams to adopt data quality best practices.

Interview Process Overview

The interview process for a Data Engineer at Alliant Credit Union is rigorous but highly practical. It is designed to test your real-world engineering skills rather than your ability to memorize abstract algorithms. You will typically begin with a recruiter phone screen to discuss your background, your interest in the credit union, and high-level technical alignment. This is followed by a technical screen, often conducted via video, where you will face practical SQL challenges and data troubleshooting scenarios.

If you advance, you will participate in a comprehensive virtual onsite loop. This stage usually consists of three to four sessions covering data architecture, advanced SQL and pipeline engineering, and behavioral assessments. Alliant Credit Union places a strong emphasis on situational problem-solving. You can expect interviewers to present you with actual, sanitized scenarios that the data team has faced, asking you to walk through your debugging and development process.

The company values transparency, collaboration, and a member-first mindset. Throughout the process, interviewers will gauge how well you align with these core values, looking for candidates who are not just technical executors, but thoughtful data advocates.

The visual timeline above outlines the standard progression from your initial recruiter screen through to the final onsite loop and offer stage. You should use this map to pace your preparation, focusing heavily on core SQL and data concepts early on, and shifting toward architecture and behavioral storytelling as you approach the final rounds. Keep in mind that specific team requirements or scheduling constraints may slightly alter the sequence of the onsite interviews.

Deep Dive into Evaluation Areas

To succeed in your interviews, you must demonstrate mastery across several key technical and behavioral domains. Interviewers will probe deeply into your past experiences to understand how you build, test, and maintain data systems.

SQL, Data Modeling, and Query Optimization

• SQL is the foundational language for this role, and you will be tested heavily on your ability to write complex, efficient queries. Interviewers want to see that you can manipulate large datasets, handle complex joins, and optimize queries that are performing poorly.
• Advanced Aggregations – Expect to use window functions, CTEs (Common Table Expressions), and complex grouping sets to generate analytical datasets.
• Data Modeling – You must understand dimensional modeling (Star and Snowflake schemas) and how to design tables that support both transactional integrity and analytical performance.
• Performance Tuning – Be prepared to explain how you analyze execution plans, use indexing effectively, and refactor queries to reduce compute costs.
• Advanced concepts (less common) – Recursive CTEs, dynamic SQL generation, and specific dialect optimizations (e.g., handling skew in distributed databases).

Example questions or scenarios:

• "Write a query to identify duplicate transactions within a 10-minute window for the same member account."
• "Walk me through how you would optimize a stored procedure that takes hours to run, assuming you cannot change the underlying hardware."
• "Given a highly normalized database schema, design a dimensional model to track daily account balance snapshots."

Data Quality, Governance, and Testing

• As a Senior Data Quality Developer, this is your primary domain. You will be evaluated on your methodology for ensuring data remains accurate, complete, and consistent across multiple systems. Strong performance means treating data quality as a proactive engineering discipline rather than a reactive operational task.
• Validation Frameworks – You will need to discuss how you build automated checks for nulls, referential integrity, and business logic constraints during the ETL process.
• Anomaly Detection – Explain how you identify statistical outliers or unexpected volume drops in daily data feeds.
• Data Lineage and Metadata – Be ready to discuss how you track data from its source to its final destination, ensuring traceability for compliance and debugging.
• Advanced concepts (less common) – Implementing machine learning models for anomaly detection, or building custom data profiling libraries from scratch.

Example questions or scenarios:

• "How would you design an automated alerting system to notify the team if a daily third-party financial feed is missing 20% of its expected records?"
• "Describe a time you discovered a critical data error in production. How did you trace the root cause and implement a permanent fix?"
• "What metrics do you use to define and measure 'data quality' for an executive dashboard?"

ETL Pipelines and Data Architecture

• Moving data reliably is just as important as validating it. You will be assessed on your ability to design resilient data pipelines that can handle failure gracefully. Interviewers look for candidates who understand the trade-offs between batch and streaming, and who prioritize idempotency and recoverability.
• Pipeline Orchestration – You should be familiar with tools like Airflow, Dagster, or Prefect, and understand how to manage complex task dependencies.
• Idempotency and Backfilling – You must be able to design pipelines that can be rerun safely without creating duplicate records, and explain how you handle historical data restatements.
• Error Handling – Discuss your strategies for dead-letter queues, retry mechanisms, and logging within your data pipelines.
• Advanced concepts (less common) – Real-time stream processing architecture (e.g., Kafka, Flink) and complex event processing for fraud detection.

Example questions or scenarios:

• "Design an ETL pipeline that ingests daily loan origination files, applies strict quality rules, and loads the clean data into a data warehouse."
• "If a pipeline fails halfway through a multi-step transformation, how do you ensure the system recovers without data loss or duplication?"
• "Explain the architectural differences and trade-offs between an ETL approach and an ELT approach in a modern cloud environment."

Key Responsibilities

As a Senior Data Quality Developer at Alliant Credit Union, your daily routine will revolve around safeguarding the enterprise data ecosystem. You will be responsible for designing and deploying comprehensive data quality frameworks that automatically profile, validate, and monitor data as it flows from core banking systems into the data warehouse. This involves writing extensive SQL scripts, developing Python-based validation routines, and integrating these checks directly into existing ETL pipelines.

Collaboration is a massive part of this role. You will work hand-in-hand with Data Engineers to ensure pipelines are built with quality in mind from day one. You will also partner with Data Stewards, Risk Management, and Business Intelligence teams to define strict business rules and data governance policies. When data anomalies occur, you will lead the incident response, tracing the lineage of the data to identify the root cause and deploying structural fixes to prevent recurrence.

Additionally, you will drive strategic initiatives to improve the overall maturity of the data organization. This includes building dashboards that report on data health metrics, mentoring junior engineers on testing best practices, and evaluating new tools to enhance the credit union's data observability capabilities. Your work ensures that executive leadership and regulatory bodies can trust the numbers they see.

Role Requirements & Qualifications

To be highly competitive for this role at Alliant Credit Union, you need a strong blend of data engineering fundamentals and a meticulous eye for detail. The ideal candidate brings a mature engineering mindset to the often-messy world of data quality.

• Must-have skills – Expert-level SQL proficiency is non-negotiable. You must have strong programming skills in Python or Scala for writing custom validation logic. Deep experience with ETL/ELT concepts, data warehousing principles, and automated testing frameworks is required. You must also possess excellent problem-solving skills and the ability to debug complex data pipelines.
• Nice-to-have skills – Experience working within the financial services industry or dealing with strict regulatory compliance (e.g., PCI, PII) is a major advantage. Familiarity with cloud data platforms (AWS, Azure, or GCP), modern orchestration tools (like Apache Airflow), and specific data observability platforms (like Monte Carlo or Great Expectations) will make you stand out.
• Experience level – This is a senior role, typically requiring 5+ years of experience in data engineering, BI development, or data quality engineering. A proven track record of leading complex data initiatives and mentoring team members is highly valued.
• Soft skills – Exceptional communication skills are critical. You must be able to translate complex data issues into business impacts and collaborate effectively across technical and non-technical boundaries. A proactive, ownership-driven mindset is essential for success in this environment.

Common Interview Questions

The questions below are representative of the patterns and themes you will encounter during your interviews. While you should not memorize answers, you should use these to practice structuring your thoughts, writing clean code on a whiteboard or shared editor, and articulating your problem-solving methodology.

SQL and Data Manipulation

• This category tests your ability to write efficient, accurate queries to transform and analyze data.
• Write a query to find the second highest transaction amount for each member in the past 30 days.
• How would you structure a query to calculate a rolling 7-day average of account balances?
• Explain the difference between a LEFT JOIN and an INNER JOIN, and describe a scenario where using the wrong one would cause a critical data error.
• Given a table of user logins with timestamps, write a query to identify users who have logged in on three consecutive days.
• How do you approach optimizing a query that uses multiple subqueries and is causing a bottleneck?

Data Quality and Troubleshooting

• These questions evaluate your proactive strategies for monitoring data health and your reactive processes for fixing broken data.
• Walk me through the core metrics you would use to measure the quality of a newly ingested dataset.
• Tell me about a time you found a silent data failure (a pipeline that succeeded, but the data was wrong). How did you fix it?
• How do you handle situations where a business stakeholder disputes the accuracy of your data?
• Design a framework to automatically detect and alert on schema changes from an upstream API.
• What is your process for managing and resolving duplicate records in a highly transactional database?

Architecture and Pipeline Engineering

• Here, interviewers want to see how you design scalable, resilient systems that move data securely.
• Design an ELT architecture for a daily batch process that ingests millions of credit card transactions.
• How do you ensure idempotency in your data pipelines? Provide a specific example.
• Describe how you would implement a dead-letter queue for records that fail your data quality checks.
• What factors do you consider when choosing between a batch processing architecture and a streaming architecture?
• Explain how you manage environment configurations and CI/CD deployments for your data pipelines.

Behavioral and Leadership

• These questions assess your culture fit, communication skills, and ability to navigate challenges in a corporate environment.
• Tell me about a time you had to push back on a product manager or stakeholder because a data request was unfeasible or unsafe.
• Describe a project where you had to learn a completely new technology or tool on the fly.
• How do you prioritize your work when faced with multiple critical data bugs at the same time?
• Give an example of how you have mentored a junior team member or improved the engineering culture of your team.
• Why are you interested in joining Alliant Credit Union, and how does your background align with our digital-first mission?

Frequently Asked Questions

Q: How difficult is the technical screen for this role? The technical screen is rigorous but fair. You will not face obscure algorithmic puzzles; instead, expect complex SQL challenges and practical Python scripting tasks that mirror the actual day-to-day work of validating and transforming data.

Q: What differentiates a successful candidate from an average one? Successful candidates treat data quality as a software engineering discipline. They do not just write queries to find bad data; they build automated, scalable systems to prevent bad data from entering the ecosystem, and they communicate the business value of these systems clearly.

Q: Does Alliant Credit Union require financial domain experience? While financial experience is listed as a strong nice-to-have, it is not strictly required if your technical fundamentals are exceptional. However, you must demonstrate an understanding of the high stakes involved in handling sensitive, regulated financial data.

Q: What is the working model for this Chicago-based role? Alliant Credit Union typically operates on a hybrid model for local employees, blending in-office collaboration with remote flexibility. You should clarify the specific in-office expectations for this team directly with your recruiter during the initial screen.

Q: How long does the interview process typically take? The end-to-end process usually takes between three to five weeks, depending on scheduling availability. The recruiting team is generally communicative and will keep you updated on your status between rounds.

Other General Tips

• Prioritize Edge Cases: In financial data engineering, edge cases are often where the most critical bugs hide. When writing code or designing systems during the interview, explicitly call out how you handle nulls, duplicates, late-arriving data, and unexpected schema changes.

• Master the STAR Method: For behavioral questions, structure your answers using the Situation, Task, Action, Result framework. Be highly specific about the Action you took and ensure the Result includes quantifiable metrics (e.g., "reduced pipeline failure rate by 30%").

• Think Aloud During Technical Challenges: Your thought process is just as important as the final code. If you get stuck on a SQL query, explain what you are trying to achieve and the logic you are attempting to apply. Interviewers will often guide you if they understand your approach.

• Demonstrate Business Acumen: Always tie your technical decisions back to business outcomes. A strong data pipeline is not just about moving bytes efficiently; it is about ensuring the risk team can generate accurate compliance reports on time.

Summary & Next Steps

Securing a Data Engineer position at Alliant Credit Union is a unique opportunity to operate at the intersection of modern data architecture and high-stakes financial services. As a Senior Data Quality Developer, your expertise will directly safeguard the digital trust of hundreds of thousands of members. The interview process is designed to find engineers who are technically rigorous, highly accountable, and passionate about data integrity.

The salary data above provides the expected compensation range for this specific role in Chicago, IL. When evaluating this range, consider your years of experience, your proficiency with advanced data quality frameworks, and your domain expertise, as these factors will influence where you land within the band.

To excel in these interviews, focus your preparation on mastering advanced SQL, designing resilient data validation frameworks, and articulating your problem-solving methodology clearly. Remember that your interviewers are looking for a collaborative partner who can advocate for data excellence across the organization. For more detailed insights, practice scenarios, and community experiences, continue leveraging resources on Dataford to refine your technical narrative. Approach your preparation systematically, trust in your engineering fundamentals, and step into your interviews with confidence.