Software Engineer (Internship) Interview Experience
October 1, 2020
Positive ExperienceNo Offer
Process
I was reached out to by a recruiter to apply, likely through the school system. I was fast-tracked to the final round interview.
The final interview had three components:
- Coding
- Business case
- Behavioural
I bombed the coding and case interviews. The behavioural interview was challenging because it required using the STAR method, and the questions were very long with specific points that needed to be covered in my answers.
Questions
Super Stack
Integrating legacy code / reading code
Describe a time you explained something complex to someone.
Describe a time you worked in a team setting.
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Interview Statistics
The following metrics were computed from 189 interview experiences for the Capital One Software Engineer (Internship) role in United States.
Success Rate
47%
Pass Rate
Capital One's interview process for their Software Engineer (Internship) roles in the United States is fairly selective, failing a large portion of engineers who go through it.
Experience Rating
Positive77%
Neutral20%
Negative4%
Candidates reported having very good feelings for Capital One's Software Engineer (Internship) interview process in United States.
Capital One Work Experiences
4.0
Great Benefits, High Turnover
Software Engineer • Current Employee
Remote
December 13, 2022
Pros: Work-life balance is phenomenal. Good compensation and general benefits package. PTO policy is incredibly generous (along with other days off/learning days). Great senior leadership.
Cons: High turnover.
Consistently, work could be more interesting.
Culture is team dependent.
5.0
Great, relaxed place to work
Data Engineer • Former Employee
Plano, Texas, United States
September 6, 2018
Pros: Managers are genuinely interested in how you're doing and how the work is coming. They're generally very lenient if something comes up, and I was never encouraged to work overtime to get things finished faster. My internship project wasn't just thrown out; all intern projects are directly useful to the company and larger projects, and I was happy to work on one of these.
Cons: With my team, there were a lot of meetings. It was difficult to get work done sometimes because there were so many meetings.
5.0
Mastered Big Data Pipelines
Data Engineer • Current Employee
October 27, 2022
Pros: • Built complex Terabyte-scale data pipelines and presentation layers using Cascading for consumption by loan performance analysis models.
• Extracted TBs of data from SQL Server and Teradata to the One Lake.
• Worked with the Spark ecosystem using Spark SQL and Scala queries on different formats like Parquet files and CSV files.
• Implemented Spark using Scala and Spark SQL for faster testing and processing of data, responsible for managing data from different sources.
Cons: • Big Data Technologies: Hadoop, MapReduce, Hive, Pig, Scoop, Spark, Kafka.
• Machine Learning: Classification, Regression, Clustering, Feature engineering, Ensemble Learning.
• Statistics: Bayesian analysis, Time series and Multivariate data analysis.
• Languages: Python, R, SQL, Scala.
• NoSQL Databases: HBase, Cassandra, MongoDB.
• Cloud Platform: Azure, AWS.
• DBs & Tools: SQL Server, Azure Data Factory, ETL – SSIS/SSRS/SSAS, Tableau, Power BI.
• Version control: GIT, TFS, Confluence, BitBucket.
- 4.0Great Benefits, High TurnoverSoftware Engineer • Current EmployeeRemoteDecember 13, 2022Pros: Work-life balance is phenomenal. Good compensation and general benefits package. PTO policy is incredibly generous (along with other days off/learning days). Great senior leadership.Cons: High turnover. Consistently, work could be more interesting. Culture is team dependent.
- 5.0Great, relaxed place to workData Engineer • Former EmployeePlano, Texas, United StatesSeptember 6, 2018Pros: Managers are genuinely interested in how you're doing and how the work is coming. They're generally very lenient if something comes up, and I was never encouraged to work overtime to get things finished faster. My internship project wasn't just thrown out; all intern projects are directly useful to the company and larger projects, and I was happy to work on one of these.Cons: With my team, there were a lot of meetings. It was difficult to get work done sometimes because there were so many meetings.
- 5.0Mastered Big Data PipelinesData Engineer • Current EmployeeOctober 27, 2022Pros: • Built complex Terabyte-scale data pipelines and presentation layers using Cascading for consumption by loan performance analysis models. • Extracted TBs of data from SQL Server and Teradata to the One Lake. • Worked with the Spark ecosystem using Spark SQL and Scala queries on different formats like Parquet files and CSV files. • Implemented Spark using Scala and Spark SQL for faster testing and processing of data, responsible for managing data from different sources.Cons: • Big Data Technologies: Hadoop, MapReduce, Hive, Pig, Scoop, Spark, Kafka. • Machine Learning: Classification, Regression, Clustering, Feature engineering, Ensemble Learning. • Statistics: Bayesian analysis, Time series and Multivariate data analysis. • Languages: Python, R, SQL, Scala. • NoSQL Databases: HBase, Cassandra, MongoDB. • Cloud Platform: Azure, AWS. • DBs & Tools: SQL Server, Azure Data Factory, ETL – SSIS/SSRS/SSAS, Tableau, Power BI. • Version control: GIT, TFS, Confluence, BitBucket.
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