Data Engineer 4, ERM Transformation

Capital One Financial

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Quick summary

Work type: On-site
Location: Richmond, VANew York, NYMcLean, VA
Salary: $197,300–$225,100 / yr
Employment: Full-time
Posted: 1 day ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $189k

This role $211k

$161k $232k

below market most similar roles pay here above market

This role pays more than 86% of similar roles. Most pay $167,500–$211,200 — the blue band above. At the midpoint, this role pays about $211k versus about $189k for comparable roles.

Based on 240 similar postings.

Employer

About Capital One Financial

Capital One Financial is a bank holding company specializing in credit cards, auto loans, banking, and savings products, known for its data-driven approach to consumer and commercial finance. Industry: Financial Services & Banking

Capital One Financial currently has 2107 open roles on FindRole.

Listed pay typically runs $197,300–$225,100 across 1107 roles with salary data.

Most-posted roles

View all roles at Capital One Financial

At a glance

TL;DR · Data Engineer 4, ERM Transformation

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The Data Engineer 4 - ERM Transformation joins the Risk Tech team to drive a major transformation in risk management through state-of-the-art AI technology. Working with the GRC team, you will independently design, build, and deploy proprietary cloud-first data solutions and applications. Your daily responsibilities include architecting scalable data pipelines, enforcing engineering design patterns, and implementing security standards like encryption and fine-grained access control. You will utilize Python, Spark, SQL, and Scala to manage distributed data workloads across lakehouse architectures. The role requires proficiency with open-source relational and NoSQL databases, as well as cloud-based warehousing platforms like Databricks and Snowflake. You will solve complex business problems by building robust, resilient data products that use machine learning to automatically detect risks and prevent issues.

What does a Data Engineer earn in Virginia?

Median $155450 from 67 postings across 8 companies.

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Skills

Python SQL Spark Scala Java Databricks Snowflake AWS Microsoft Azure Google Cloud NoSQL MongoDB Cassandra DynamoDB Airflow Dagster Splunk EMR Glue Redshift Agile Machine Learning Lakehouse Architecture

What you'll do

Design, develop, test, and support full-stack technical solutions using Python, Spark, and cloud-based data warehousing platforms.
Build and deploy scalable, resilient data pipelines and platforms to handle increasing data volumes and business demands.
Architect and enforce common data engineering design patterns to ensure code quality, maintainability, and reusability.
Implement data security standards, including encryption at rest/transit and fine-grained access control, to ensure regulatory compliance.
Develop proprietary risk management solutions powered by state-of-the-art AI technology and distributed microservices.
Independently design and deliver world-class cloud data solutions and applications with minimal supervision.
Communicate technical concepts and data outcomes clearly to internal and external stakeholders to drive alignment.

What we're looking for

Bachelor's Degree or higher in Computer Science or a related quantitative field.
At least 4 years of experience in application development.
At least 2 years of experience in distributed data, SQL, and data pipeline design and development.
At least 2 years of experience with one programming language such as Python, Java, or Scala.
At least 1 year of experience in data modeling and designing end-to-end data solutions using relational and non-relational database systems.
7+ years of experience in application development with proficiency in Python, SQL, Scala, or Java (preferred).
4+ years of experience in public cloud environments, distributed data workloads, real-time pipelines, NoSQL databases, or data warehousing (preferred).
2+ years of experience in Agile development, data observability, or developing user-centric reusable data products (preferred).

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