McLean, VA

Data Jobs in McLean, VA

90 jobs found

Include remote jobs

4mo ago

Data Engineer - Enterprise, Architecture & Data (EAD) (contract)

Hilton McLean, VA

McLean, VA

4mo ago Closed

Contract Analytics AWS Aws Glue

This position is no longer accepting applications (closed Jun 16, 2026).

Job Description

Hilton is seeking a Data Engineer in the Enterprise, Architecture & Data (EAD) group on a contract basis to advance data architecture and enterprise data integration across Hilton’s data ecosystem. The position, based in McLean, VA with a hybrid work arrangement, requires at least three years of relevant experience and a collaborative, cross-functional approach to translating complex requirements into practical data solutions.

Responsibilities

Interpret project requirements to support new and existing systems within the data ecosystem.
Plan and organize work streams, identify data needs, and understand design and solution architectures.
Evaluate enterprise data characteristics and the current technical landscape to gauge implications for new architectures and analytical reporting.
Engage with data and business teams in Martech to align on information assets and gain hands-on experience.
Track projects through the implementation phase and brief senior leadership on status, risks, and issues.
Collaborate with internal partners across Business, Product, Technology, and Analytics functions.
Provide support to internal data teams by clarifying work and proactively addressing risks and issues.
Maintain open communication and alignment across the broader data organization.
Capture detailed requirements for new business initiatives and craft solutions to deliver on these initiatives.

Requirements

Hands-on experience with data quality, ETL, and ongoing data maintenance.
Proficiency in Talend, AWS Glue, and AWS Lambda.
Expertise with CDW and Redshift, Snowflake, and related platforms.
Strong background in cloud and hybrid data environments and platforms.
Familiarity with Amazon Web Services cloud environment.
Ability to function effectively in customer-facing settings.
Solid organizational and project-management capabilities.
Excellent communication and collaboration skills.

Technologies

Talend
AWS Glue
AWS Lambda
CDW
RedShift
Snowflake
AWS
Amazon Web Services

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4mo ago

Data Engineer (Bid Role starting in 10 weeks) - TS/SCI w/ Poly

Leading Path Consulting McLean, VA

McLean, VA

4mo ago Closed

$175k - $275k/yr Contract Apache Airflow Automation AWS

This position is no longer accepting applications (closed Jun 16, 2026).

Job Description

Leading Path Consulting has a Data Engineer bid role for a planned start in ten weeks. The position is based onsite in McLean, VA, with a salary range of USD 175,000 to 275,000 per year, and a focus on assessing, designing, and implementing robust data infrastructure and pipelines across the organization, emphasizing data quality, observability, and governance. This bid role requires an active TS/SCI with FS poly.

Responsibilities

Conduct comprehensive assessments of current data pipelines, infrastructure, and data flows, including integrations with ServiceNow, network-management tools, and business applications, to identify technical debt, bottlenecks, and reliability issues.
Evaluate current data architecture against industry best practices and organizational needs; develop technical recommendations and a roadmap for data infrastructure improvements.
Design, implement, and maintain production-grade data pipelines using orchestration platforms such as Airflow or Prefect.
Develop resilient ETL and ELT processes from diverse sources including SaaS platforms, network management systems, databases, APIs, files, and streams.
Build API integrations with authentication methods (OAuth, API keys, SSO), plus rate limiting, pagination, retry logic, and error handling.
Extract data from systems not designed for export, reverse-engineering undocumented data structures and relationships.
Manage semi-structured data (JSON and XML) and transform it into structured datasets with consistent schemas.
Design dimensional models, data warehouses, and data marts following industry methodologies.
Create conceptual, logical, and physical data models optimized for query performance and storage efficiency.
Implement slowly changing dimensions and other data warehousing patterns.
Establish naming conventions, data standards, and modeling best practices.
Implement comprehensive data quality checks, validation rules, and automated monitoring with alerting.
Embed error handling, failure recovery, logging, and observability into all processes.
Optimize pipelines for performance, cost, and resource utilization.
Develop reusable components and frameworks; refactor legacy pipelines for reliability.
Build and maintain data infrastructure on cloud platforms (AWS, Azure, or GCP) using infrastructure-as-code with Terraform and CloudFormation.
Implement CI/CD pipelines, version control with Git, and automated testing frameworks.
Manage database performance tuning, indexing, partitioning, and capacity planning.
Establish backup, recovery, security controls, access controls, and compliance measures.
Partner with analysts, software developers, and business stakeholders to translate requirements into technical solutions.
Create comprehensive documentation for systems, processes, and integrations.
Provide technical guidance on data availability and proper usage; enable self-service access.
Troubleshoot pipeline failures, performance issues, and data discrepancies; perform root cause analysis.

Requirements

Active TS/SCI with FS poly required prior to application.
Proven experience building and managing data pipelines.
Hands-on experience with Python.
Experience with cloud computing using AWS services.
Experience processing data with Apache Spark.
Experience with a relational database management system (PostgreSQL, Oracle, MySQL) and writing SQL queries.
Experience working in Linux environments and with shell scripting.
Experience analyzing data in formats such as CSV, XML, JSON, Avro, Parquet, etc.
Experience writing and validating unit tests.

Technologies

Airflow
Prefect
Python
AWS, Azure, GCP
Apache Spark
PostgreSQL
Oracle
MySQL
Linux
Shell scripting
JSON, XML, Avro, Parquet
OAuth, Single Sign-On (SSO)
Terraform, CloudFormation
Git
ServiceNow
NetIM
Forward Networks
Hive
Iceberg
EMR

Benefits

Fully paid medical, dental, and vision premiums
Generous paid time off
11 paid holidays
6% 401(k) contribution
Annual training and tuition reimbursement
SPOT Award bonuses
Regular team events
Opportunities for professional growth and advancement

Travel

Travel is anticipated for this contract, pending sponsor approval. Local travel or point-of-view travel within the local place of performance is expected on an as-needed basis.

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4mo ago

Lead Machine Learning Engineer

Capital One McLean, VA

McLean, VA

4mo ago Closed

$197k - $225k/yr Full time Artificial Intelligence Azure Ml Cloud Operations

This position is no longer accepting applications (closed Jun 13, 2026).

Job Description

Capital One is seeking a Lead Machine Learning Engineer to drive productionization of ML applications at scale within an Agile team. This onsite role in McLean, VA offers a salary range of USD 197,300 to 225,100 per year and centers on responsible AI, scalable data pipelines, and robust ML infrastructure developed in collaboration with Product and Data Science teams.

Responsibilities

Develop and deploy ML models and components to address real-world business challenges, in partnership with Product and Data Science teams.
Guide ML infrastructure decisions by applying knowledge of modeling techniques and issues, including model choice, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation.
Tackle complex problems by coding, validating ML models, and implementing automated tests and deployment processes.
Work within a cross-functional Agile team to build and enhance software powering state-of-the-art big data and ML workloads.
Retrain, maintain, and monitor models in production environments.
Leverage or develop cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale.
Design efficient data pipelines to feed ML models.
Apply CI/CD best practices, including automated testing and monitoring, to ensure reliable deployment of ML models and code.
Maintain secure, well-governed code and models, adhering to responsible and explainable AI principles.
Proficient in Python, Scala, or Java.

Requirements

Bachelor’s Degree
At least 6 years of experience designing and building data-intensive solutions using distributed computing (internship experience does not apply)
At least 4 years of experience programming with Python, Scala, or Java
At least 2 years of experience building, scaling, and optimizing ML systems

Technologies

Python
Scala
Java
scikit-learn
PyTorch
Dask
Spark
TensorFlow
AWS
Azure
Google Cloud Platform

Benefits

Health benefits
Financial benefits
Performance-based incentive compensation (cash bonuses and/or long-term incentives)

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4mo ago

Sr. Lead, Machine Learning Engineer (Enterprise Platforms Technology)

Capital One McLean, VA

McLean, VA

4mo ago Closed

$230k - $262k/yr Full time Deep Learning DevOps Machine Learning

This position is no longer accepting applications (closed Jun 13, 2026).

Job Description

Capital One's Enterprise Platforms Technology (EPTech) team offers a path to productionize machine learning at scale, with a focus on architectural design, development, and governance of ML models and infrastructure. This onsite role in McLean, VA comes with a competitive annual compensation range of USD 229,900 to 262,400, plus a benefits package designed to support health, financial security, and total well-being.

Responsibilities

Design, build, and deliver ML models and components that address real business needs, partnering with Product managers and Data Science teams.
Inform ML infrastructure decisions with a solid understanding of modeling techniques, including model choice, data and feature selection, training, hyperparameter tuning, dimensionality, bias vs. variance, and validation.
Solve complex problems by writing and validating application code, developing ML models, and automating tests and deployment processes.
Collaborate within a cross functional Agile team to create and enhance software that enables state of the art big data and ML applications.
Retrain, maintain, and monitor models once they are in production.
Leverage or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale.
Construct optimized data pipelines to feed ML models.
Adopt continuous integration and continuous deployment practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
Ensure code quality and governance to reduce vulnerabilities, maintain risk-aware practices for models, and follow Responsible and Explainable AI principles.
Work with Python, Scala, or Java to implement solutions.

Requirements

Bachelor’s degree; Master’s or doctoral degree in computer science, electrical engineering, mathematics, or a related field.
At least 8 years of experience designing and building data-intensive solutions using distributed computing (internship experience does not apply).
At least 4 years of experience programming with Python, Scala, or Java.
At least 3 years of experience building, scaling, and optimizing ML systems.
At least 2 years of experience leading teams developing ML solutions.
Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform.
4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow.
3+ years of experience developing performant, resilient, and maintainable code.
3+ years of experience with data gathering and preparation for ML models.
3+ years of people management experience.
ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents.
3+ years of experience building production-ready data pipelines that feed ML models.
Ability to communicate complex technical concepts clearly to a variety of audiences.

Technologies

Python, Scala, Java
scikit-learn, PyTorch, Dask, Spark, TensorFlow
AWS, Azure, Google Cloud Platform

Benefits

Health benefits
Financial benefits
Incentives (cash bonus and/or long-term incentives)
Other benefits that support total well-being

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4mo ago

Lead Machine Learning Engineer (Enterprise Platforms Technology)

Capital One McLean, VA

McLean, VA

4mo ago Closed

$197k - $225k/yr Full time AWS Azure Big Data

This position is no longer accepting applications (closed Jun 9, 2026).

Job Description

This on-site role in McLean, VA focuses on productionizing ML applications and systems at scale within Capital One's Enterprise Platforms Technology group.

Responsibilities

Design, build, and deliver ML models and components that address real world business problems, in collaboration with Product and Data Science teams.
Inform ML infrastructure decisions through understanding of modeling techniques and issues, including model selection, data, feature engineering, model training, hyperparameter tuning, dimensionality, bias/variance, and validation.
Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
Collaborate within a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications.
Retrain, maintain, and monitor models in production.
Leverage or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale.
Construct optimized data pipelines to feed ML models.
Apply continuous integration and continuous deployment practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
Ensure code is well governed to minimize vulnerabilities, and that models meet risk and Responsible AI / Explainable AI standards.
Use programming languages such as Python, Scala, or Java.

Requirements

Bachelor’s Degree.
At least 6 years of experience designing and building data-intensive solutions with distributed computing (internship experience does not apply).
At least 4 years of experience programming with Python, Scala, or Java.
At least 2 years of experience designing, scaling, and optimizing ML systems.

Technologies

Python, Scala, Java
scikit-learn, PyTorch, Dask, Spark, TensorFlow
AWS, Azure, Google Cloud Platform

Benefits

Health benefits
Financial benefits
Performance-based incentive compensation (cash bonuses and/or long-term incentives)

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4mo ago

Senior Lead Machine Learning Engineer

Capital One McLean, VA

McLean, VA

4mo ago Closed

$230k - $262k/yr Full time AWS Azure Ml Cloud Architecture

This position is no longer accepting applications (closed Jun 9, 2026).

Job Description

Capital One is seeking a Senior Lead Machine Learning Engineer to drive the productionization of ML applications at scale within an Agile team. The role centers on shaping ML architecture, reviewing model and application code, and ensuring the high availability and performance of ML systems in production.

Location: McLean, VA (onsite)

Salary: USD 229,900 - 262,400 per year

Responsibilities

Design, build, and deliver ML models and components that address real business needs, collaborating with Product and Data Science teams.
Inform ML infrastructure decisions with a solid understanding of modeling techniques, including model choice, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation.
Develop and test application code, train and validate ML models, and automate tests and deployment.
Collaborate within a cross-functional Agile team to create and enhance software enabling state-of-the-art big data and ML applications.
Retrain, maintain, and monitor models in production environments.
Leverage or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale.
Construct optimized data pipelines to feed ML models.
Apply continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure reliable deployment of ML models and application code.
Ensure code quality and governance to reduce vulnerabilities, uphold risk standards, and follow Responsible and Explainable AI practices.
Work with programming languages such as Python, Scala, or Java.

Requirements

Bachelor's Degree
At least 8 years of experience designing and building data-intensive solutions using distributed computing; internship experience does not count toward this requirement.
At least 4 years of experience programming with Python, Scala, or Java
At least 3 years of experience building, scaling, and optimizing ML systems
At least 2 years of experience leading teams developing ML solutions

Technologies

Python, Scala, Java
scikit-learn, PyTorch, Dask, Spark
Kubeflow, TensorFlow
AWS, Azure, Google Cloud Platform

Benefits

Performance-based incentive compensation
Health benefits
Financial benefits

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4mo ago

Sr. Distinguished Machine Learning Engineer

Capital One McLean, VA

McLean, VA

4mo ago Closed

$315k - $359k/yr Full time Senior Cloud Architecture DevOps

This position is no longer accepting applications (closed Jun 9, 2026).

Job Description

Capital One offers a performance-based incentive program with cash bonuses and long-term incentives. This onsite, McLean, VA opportunity places you in a senior leadership role focused on bringing machine learning to production at scale. You will guide architecture and modeling efforts, oversee the development of robust ML systems, and mentor engineers across cross-functional teams.

Responsibilities

Deliver machine learning models and software components that address complex business problems in financial services, collaborating with Product, Architecture, Engineering, and Data Science teams.
Shape and advance ML models and supporting software to enable advanced, intelligent systems.
Lead large scale ML initiatives with the end user in mind.
Utilize cloud based architectures and technologies to deploy optimized ML models at scale.
Refine data pipelines to feed ML models efficiently.
Work with Python, Scala, and C or C++ across development tasks.
Leverage compute frameworks such as Dask and RAPIDS to accelerate processing.
Promote best practices across engineering and modeling lifecycles.
Contribute to recruiting, developing, and retaining top engineering talent.

Requirements

Bachelor’s degree.
At least 10 years designing and building data‑intensive solutions using distributed computing.
At least 7 years programming in C, C++, Python, or Scala.
At least 4 years of experience with the full ML development lifecycle in a business critical setting.