Senior Data Engineer (W2)

Job Title: Senior Data Engineer

Location: McLean, VA (Local candidates preferred)

Employment Type: Contract

Job Summary

We are seeking a highly experienced Senior Data Engineer with 14+ years of hands-on experience in designing, developing, and managing large-scale, high-performance data infrastructure. The ideal candidate will play a key role in building robust data and ML infrastructure, enabling seamless data flow and analytics across the enterprise.

Responsibilities

• Data Infrastructure Development: Design, build, and maintain scalable, reliable, and secure data infrastructure to support enterprise data needs.
• ML Infrastructure: Collaborate with the Machine Learning and Data Science teams to develop and maintain infrastructure for ML model training, testing, and deployment.
• Pipeline Management: Develop, optimize, and manage end-to-end data pipelines from ingestion to processing and storage ensuring high availability and performance.
• Data Lake Architecture: Architect and implement data lake and warehouse solutions for large-scale structured and unstructured data.
• Collaboration: Partner with data scientists, product managers, and cross-functional engineering teams to align data infrastructure with business objectives.
• Automation & Optimization: Identify opportunities for automation, scalability, and performance improvement within data ecosystems.
• Governance & Security: Ensure data quality, compliance, and governance best practices are followed.

Qualifications

• Experience: 10 14+ years of experience in Data Engineering, Data Analysis, or related domains, with proven success managing large-scale data ecosystems.
• Technical Expertise:
• Strong hands-on experience with Kafka, Spark, Airflow, Snowflake, Databricks, and modern ETL frameworks.
• Expertise in data lakes, data warehousing, and streaming architectures.
• Experience with cloud services (AWS preferred; Google Cloud Platform/Azure also acceptable).
• Programming Skills: Proficient in Python, Java, or Scala for data processing and automation.
• Frameworks: Deep understanding of Apache Spark, Flink, or Beam for large-scale distributed data processing.
• Databases: Experience with SQL and NoSQL databases (e.g., Postgres, MongoDB, Cassandra).
• ML Infrastructure: Exposure to ML Ops tools, pipelines, and infrastructure optimization for model deployment.
• System Design: Strong architectural mindset ability to design for scalability, fault tolerance, and high availability.
• Communication: Excellent communication skills, with the ability to translate complex technical details into clear business insights.
• Education: Bachelor s or Master s degree in Computer Science, Engineering, or a related technical field (or equivalent experience).
• Experience with Terraform, Kubernetes, or Docker for infrastructure automation.
• Familiarity with data governance, lineage, and cataloging tools (e.g., Collibra, Alation).
• Knowledge of CI/CD pipelines for data and ML workflows.