Senior Data Engineer (Only Local to VA) (Only W2)

Job Title: Senior Data Engineer (Only W2)

Location: McLean, Virginia, United States (Only Local to Virginia)

Job Type: Contract - W2

Experience: Min 10 Years

We are seeking an experienced and highly skilled Senior Data Engineer to join our dynamic team. In this role, you will leverage your deep technical expertise to design, implement, and optimize complex data architectures and pipelines, ensuring the reliability, scalability, and efficiency of our data infrastructure. You will work closely with data scientists, analysts, and other engineers to build and maintain data solutions that enable actionable insights and drive business decisions.

As a Senior Data Engineer with 10 years of experience, you will take ownership of large-scale data systems, mentor junior engineers, and play a key role in shaping the future of our data strategy. You will have the opportunity to work with cutting-edge technologies and be involved in the end-to-end data lifecycle.

• Data Architecture & Design: Lead the design and implementation of complex data systems, including data warehouses, data lakes, and ETL pipelines. Architect scalable, fault-tolerant, and high-performance data solutions to meet business requirements.
• Data Pipeline Development: Build and optimize robust, high-performance ETL/ELT pipelines to process large volumes of structured and unstructured data from various sources. Ensure data integrity, consistency, and quality across multiple environments.
• Data Integration: Integrate data from disparate sources including APIs, external vendors, cloud platforms, and on-premises systems into a centralized data repository or cloud-based infrastructure.
• Collaboration & Mentorship: Work closely with data scientists, analysts, product teams, and business stakeholders to understand data requirements and deliver actionable insights. Mentor junior engineers and guide them in best practices for data engineering and development.
• Performance Tuning & Optimization: Monitor, troubleshoot, and optimize the performance of data systems and pipelines, ensuring fast and reliable data availability.
• Automation & Efficiency: Automate repetitive tasks and optimize workflows to improve data processing speed, scalability, and efficiency.
• Data Security & Compliance: Ensure data governance standards, security protocols, and privacy regulations are adhered to across all data-related systems and processes.
• Documentation & Best Practices: Maintain detailed documentation for data systems, processes, and best practices. Promote a culture of high-quality code and data integrity.
• Innovation & Research: Stay up to date with the latest trends, tools, and technologies in data engineering and related fields. Proactively propose improvements to our data infrastructure and tooling.

• Experience: Minimum 10 years of experience as a Data Engineer, with at least 3-5 years in a Senior Data Engineer or Lead Data Engineer role.
• Technical Expertise:
• Expertise in designing and implementing data pipelines using tools like Apache Spark, Apache Kafka, Airflow, or similar technologies.
• Proficiency in SQL, NoSQL databases (e.g., MongoDB, Cassandra), and data warehouse solutions (e.g., Snowflake, Redshift, BigQuery).
• Hands-on experience with cloud platforms (AWS, Azure, GCP) and cloud-native data solutions (e.g., AWS Glue, Lambda, Dataflow).
• Experience with programming languages such as Python, Java, or Scala for data engineering tasks.
• Deep understanding of data modeling, ETL/ELT processes, and batch vs. real-time data processing.
• Familiarity with containerization and orchestration tools like Docker, Kubernetes, and Terraform.
• Leadership & Collaboration: Proven experience working in a collaborative environment, leading data initiatives, and mentoring teams. Ability to communicate complex technical concepts to both technical and non-technical stakeholders.
• Problem-Solving: Strong analytical and problem-solving skills with a focus on finding scalable and efficient solutions to complex data challenges.
• Data Governance & Security: Experience with data security protocols, privacy regulations (GDPR, CCPA), and best practices in data governance.
• Agile Methodology: Experience working in an Agile environment and familiarity with Scrum or Kanban methodologies.

• Advanced Degree: Master’s or PhD in Computer Science, Engineering, Data Science, or a related field.
• Data Science & Analytics: Familiarity with machine learning algorithms, data science workflows, or data analytics tools.
• DevOps/CI-CD: Experience with continuous integration/continuous deployment (CI/CD) processes in a data engineering context.
• Business Acumen: Ability to translate business needs into technical solutions and understand the strategic objectives of the organization.