Distinguished Data Engineer

Capital One Capital One · Banking · McLean, VA +3

Distinguished Data Engineer role focused on driving technical strategy for enterprise-scale data pipelines, leveraging AWS, Lakehouse architecture, Kafka, Flink, Spark, Airflow, Snowflake, and Databricks. The role involves building awareness, promoting engineering excellence, influencing stakeholders, and mentoring talent. Preferred qualifications include experience deploying ML models and Agentic AI solutions, and data observability tooling.

What you'd actually do

  1. Build awareness, increase knowledge and drive adoption of modern technologies, sharing consumer and engineering benefits to gain buy-in
  2. Strike the right balance between lending expertise and providing an inclusive environment where others’ ideas can be heard and championed; leverage expertise to grow skills in the broader Capital One team
  3. Promote a culture of engineering excellence, using opportunities to reuse and innersource solutions where possible
  4. Effectively communicate with and influence key stakeholders across the enterprise, at all levels of the organization
  5. Operate as a trusted advisor for a specific technology, platform or capability domain, helping to shape use cases and implementation in an unified manner

Skills

Required

  • Bachelor’s Degree
  • 9 years of experience in data engineering
  • 5 years of experience in data architecture
  • 3 years of experience building applications in AWS
  • 3 years of Python, Scala, Java, or SQL
  • 3 years of Flink or Spark
  • 3 years of Kafka
  • 3 years of Delta Lake or Iceberg
  • 3 years of building highly performant, highly resilient Big Data solutions

Nice to have

  • Masters’ Degree
  • 10+ years of experience in data engineering
  • 3+ years of data modeling experience
  • 2+ years of experience with ontology standards for defining a domain
  • 3+ years of experience deploying machine learning models
  • 1+ year of experience deploying Agentic AI solutions
  • 3+ years of experience implementing big data processing solutions on AWS or other cloud providers
  • 3+ years of Snowflake or Databricks
  • 3+ years of Data Observability tooling (lineage, data quality, monitoringa or alerting)
  • 3+ years of detecting and remediating

What the JD emphasized

  • deep expertise in AWS infrastructure, Lakehouse architecture, Kafka, Flink, Spark, Airflow, Snowflake, and Databricks
  • deploying machine learning models
  • deploying Agentic AI solutions
  • Data Observability tooling

Other signals

  • AWS infrastructure
  • Lakehouse architecture
  • Kafka
  • Flink
  • Spark
  • Airflow
  • Snowflake
  • Databricks
  • deploying machine learning models
  • deploying Agentic AI solutions
  • Data Observability tooling