Lead data engineering initiatives, design and implement data pipelines, and drive strategic direction of the data engineering practice at Capital One.
Locations (2)
McLean, Virginia, United States
Richmond, Virginia, United States
Compensation
The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $269,100 - $307,200 for Staff Data Engineer Richmond, VA: $244,700 - $279,200 for Staff Data Engineer
Responsibilities
Build awareness, increase knowledge and drive adoption of modern technologies
Promote a culture of engineering excellence
Effectively communicate with and influence key stakeholders across the enterprise
Operate as a trusted advisor for a specific technology, platform or capability domain
Lead the way in creating next-generation talent for Tech
Requirements
Bachelor's Degree in Computer Science or a related quantitative field
Nice to have: Master’s Degree in Computer Science
Nice to have: 10+ years of experience in data engineering
Nice to have: 6+ years of data modeling experience
Nice to have: 2+ years of experience with ontology standards
Lead cloud software, data and AI technologies for spine and cranial treatment and intervention in a global role within Medtronic’s Neuroscience organization.
Compensation
Salary ranges for U.S (excl. PR) locations (USD):$178,400.00 - $267,600.00
Responsibilities
Define and drive short- and long-range product strategies for AI-enabled and cloud SaaS solutions
Identify and prioritize unmet clinical, operational, and economic needs across surgeons, hospital administrators, and health systems
Shape product vision, roadmap, and positioning across the full product lifecycle
Lead market research, segmentation, and competitive analysis across cranial and spine surgery, and AI ecosystems
Translate surgeon and market insights into differentiated product requirements and value propositions
Requirements
Bachelor's Degree
Nice to have: product management
Nice to have: upstream marketing
Nice to have: strategy roles within software, AI, or digital health
Nice to have: experience bringing complex technical products to market
This role will have a primary focus on day-to-day administration, monitoring, and support of the Enterprise Information Management (EIM) platform, participate in management of the Cloud infrastructure, and design, build, and operate process automations to manage the platform.
Compensation
Base Pay Scale: Generally starting at $83.98 - $111.27 per hour
Responsibilities
Manage, in a highly automated manner, the infrastructure of SHC's Enterprise Information Management (EIM) platform
Collaborate with owners of Data Products and other solutions to align solutions
Provide subject matter leadership for one or more domains and/or operational frameworks (SecOps, DevOps, FinOps, Architecture, etc.)
Drive evolution of related portions of the EIM architectural blueprint and roadmap
Manage the financial operations (FinOps) for their domain(s)
Build and develop models that will feed directly into regulatory reporting and consumption component of the water balance
Enrich stakeholder insight of our Big Data Smart Meter set, applying quality assurance techniques and automation processes to deliver consistent reporting
Develop statistical and predictive models that improve understanding of consumption, per capita consumption, business demand and leakage, and feed directly into regulatory reporting
Carry out exploratory analysis, data mining and empirical analysis to identify trends, drivers and opportunities
Apply data science techniques such as linear and multivariate regression, decision trees, Monte Carlo analysis and neural networks to complex business problems
Design and build robust batch and real‑time data pipelines on GCP
Own data quality within delivery area, defining and implementing data contracts, quality checks, and observability instrumentation
Develop and maintain in‑house connectors and third‑party native integrations for IG’s ingestion estate
Contribute to engineering standards and best practices across the squad, including code review, dbt modelling patterns, CI/CD pipeline hygiene, and change control for shared datasets
Support feature engineering and model data pipelines for Data Science, helping lay foundations for AI‑ready data
Level 5 or higher qualification in Data Science, Computer Science, Statistics, Mathematics or a related quantitative subject, or equivalent professional experience
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