Senior Data Engineer (Remote, Virginia) — Capital One Software

Are you a skilled Senior Data Engineer ready to build cloud-scale data platforms? This Capital One remote role (Senior Data Engineer) focuses on big data, streaming, and data warehousing technologies like Databricks and Snowflake. Apply via the listing or EMAIL to apply directly.

Role Overview

Capital One is hiring a Senior Data Engineer to join Capital One Software — working remotely to design, develop, and scale data systems used across the company’s cloud products (Slingshot, Databolt). You will collaborate with product teams, implement robust ETL/ELT pipelines, and optimize distributed systems for performance and reliability.

What You’ll Do

• Design, develop, test, and deploy scalable data solutions across full-stack data environments.
• Work with technologies like Java, Scala, Python, Databricks, Snowflake, Kafka, and Spark.
• Collaborate with cross-functional Agile teams — product, ML, and engineering peers — to deliver cloud-based data products.
• Perform unit tests, code reviews, and tuning to ensure high-quality, performant systems.
• Mentor engineers and participate in technical communities to drive best practices.

Minimum & Preferred Qualifications

• Basic: Bachelor’s degree, 3+ years application development, 1+ year in big data technologies.
• Preferred: 5+ years development (Python, SQL, Scala, Java), 2+ years public cloud (AWS/Azure/GCP), 3+ years distributed computing (Spark, Kafka, Hadoop), NoSQL and data warehousing (Databricks/Snowflake).
• Strong UNIX/Linux skills, Agile experience, and real-time streaming knowledge are a plus.

Compensation & Location

Salary Range (Remote / Richmond, VA): $144,200 – $164,600 (annual). This role may include performance-based incentives and benefits as detailed on Capital One’s careers site.

How to Apply

Interested candidates should apply via the official job posting. For convenience, use the direct apply link or EMAIL to apply (the word EMAIL is linked for quick access):

Apply Here

EMAIL to apply

Sample Application Templates (Previews)

Sample CV (Preview)

Sample Cover Letter (Preview)

Sample Motivation Letter (Preview)

Sample Outreach / Referral Email (Preview)

Interview Preparation Guide — Senior Data Engineer

Possible Role-Specific Questions (8–12)

• Describe a production streaming pipeline you designed. What were the challenges and outcomes?
• How do you optimize Spark jobs for memory and speed?
• Explain a time you migrated data workloads to Databricks or Snowflake.
• How do you handle data schema changes in streaming systems?
• Describe your experience with Kafka: partitioning, retention, and consumer scaling.
• How do you ensure data quality across ETL/ELT pipelines?
• Walk us through a performance tuning you implemented on a data warehouse.
• How do you design for failure tolerance and observability in data systems?

General Interview Questions (5–7)

• Tell me about a time you disagreed with a technical decision and how you handled it.
• How do you prioritize technical debt versus new feature development?
• Describe your approach to mentoring junior engineers.
• How do you stay current with data engineering trends?
• What are your career goals in data engineering?

Suggested Talking Points / Answers

• For pipelines: focus on design, trade-offs (latency vs throughput), monitoring, and business impact.
• For Spark tuning: discuss partitioning, caching, broadcast joins, shuffle minimization.
• For migrations: emphasize testing, phased rollouts, data validation, rollback plans.
• For reliability: mention idempotence, retries, dead-letter queues, and robust alerting.
• For mentoring: share examples of code reviews, knowledge sessions, and paired programming.

Do’s & Don’ts (6–8 each)

• Do: Prepare concrete examples with metrics and outcomes.
• Do: Practice whiteboard/system-design explanations.
• Do: Bring clear explanations of trade-offs you made.
• Do: Ask clarifying questions before answering system-design prompts.
• Do: Show familiarity with cloud-native tooling and infra.
• Do: Demonstrate testing and observability practices.

• Don’t: Ramble without structure — use Situation, Action, Result.
• Don’t: Overclaim expertise in tools you haven’t used.
• Don’t: Skip security and compliance considerations when relevant.
• Don’t: Ignore questions about scalability or costs.
• Don’t: Be vague about your role in team projects.

Preparation Checklist

• Read Capital One’s engineering and product pages to understand mission and products.
• Prepare 3–4 STAR-format examples (design, incident response, migration, optimization).
• Review Spark, Kafka, Databricks, Snowflake fundamentals and sample queries.
• Have laptop, webcam, and a quiet space ready; test audio/video beforehand.
• Prepare questions for the interviewer about team, tech stack, and roadmap.

Extra Pro Tips

• Bring a one-page architecture diagram of a pipeline you built — visual aids impress.
• Quantify impact: percent improvement, reduced latency, cost savings, SLAs met.
• Reference open-source tools or blog posts you contributed to — shows community engagement.
• If asked a whiteboard question, verbalize your assumptions and constraints first.

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