Senior Data Engineer

• Must be a U.S. Citizen.
• Bachelors Degree or Above in Systems Engineering Computer Science or related field.
• An active security clearance or the ability to obtain one is required.
• Minimum 6 years of experience to include:
• Experience in data pipelines utilizing advanced analytics tools and platforms and Python.
• Experience in scripting tooling and automating large-scale computing environments.
• Extensive experience with major tools such as Python Pandas PySpark NumPy SciPy SQL and Git; Minor experience with TensorFlow PyTorch and Scikit-learn.
• Location: Preferred local to the Vienna Va area and able to work on-site at our Vienna VA office (3 or more days/week). Hybrid opportunities at supervisors discretion.

Data Modeling and Design

• Advanced data modeling (conceptual logical and physical) with emphasis on scalability and maintainability.
• Strong understanding of database paradigms (relational NoSQL graph time-series and document-based).
• Expertise with modern data warehousing platforms (Redshift Snowflake BigQuery).
• Deep understanding of dimensional modeling (star/snowflake schemas) and data vault techniques.
• Experience designing for both OLTP and OLAP workloads.
• Proficiency with schema evolution metadata-driven pipelines and data versioning strategies.
• Implementing data retention archival and lifecycle policies.
• Project Experience:
• Delivered optimized production-grade data models supporting analytics reporting and ML workflows aligning with established architecture and performance standards.

Data Pipeline Development

• Hands-on experience with distributed processing tools (Apache Kafka Airflow Spark Flink NiFi).
• Skilled in building and orchestrating batch and real-time pipelines on cloud platforms (AWS Glue GCP Dataflow Azure Data Factory).
• Deep understanding of incremental processing idempotency schema evolution and backfill logic.
• Proficient in pipeline automation observability and monitoring (metrics logging alerting).
• Strong Python development for ETL modular testable reusable and performance-optimized.
• Knowledge of workflow dependency management retries and failure recovery strategies.
• Project Experience:
• Owned the end-to-end design and implementation of fault-tolerant high-throughput pipelines integrating diverse data sources while maintaining data quality and SLAs.

Cloud Platforms and Services

• Deep expertise in AWS GCP or Azure data ecosystems.
• Experience building and managing cloud-native data solutions (Data Lakes Data Warehouses Data Mesh).
• Strong understanding of cloud storage (S3 Blob) managed databases (RDS DynamoDB) and compute (EMR Dataproc ECS).
• Cost governance and performance optimization for large-scale data workloads.
• Knowledge of serverless data patterns (AWS Lambda Athena GCF BigQuery).
• Experience with hybrid/multi-cloud architecture and inter-cloud data movement.
• Project Experience:
• Led migration of legacy ETL workflows and data systems to cloud-native architectures delivering measurable cost scalability and performance improvements.

Big Data Technologies

• Hands-on experience with distributed computing frameworks (Hadoop Spark Hive Presto).
• Proficiency with data lake and lakehouse architectures (Delta Lake Apache Iceberg Apache Hudi).
• Understanding of partitioning data compaction schema evolution and ACID compliance.
• Strong knowledge of query optimization on massive datasets (Athena Trino Presto).
• Performance tuning in petabyte-scale distributed systems.
• Project Experience:
• Built and maintained data platforms capable of processing structured and unstructured data at scale enabling advanced analytics and data science workloads.

Database Administration and Optimization

• Advanced SQL/NoSQL query tuning indexing sharding and partitioning strategies.
• Proficient with replication backups and disaster recovery across distributed systems.
• Skilled in analyzing query execution plans and applying cost-based optimization.
• Experience optimizing data-intensive application code and database interfaces.
• Familiarity with temporal tables data versioning and caching strategies.
• Project Experience:
• Improved query performance and system scalability through advanced indexing schema refactoring and distributed database optimization.

Data Governance and Security

• Implementing data privacy and compliance frameworks (GDPR CCPA).
• Experience with data cataloging lineage and metadata management (DataHub Collibra Alation).
• Role-based access control and sensitive data protection across multi-tenant systems.
• Integration of data quality validation and data contract testing within CI/CD pipelines.
• Automation of governance and security policies using cloud-native tools.
• Project Experience:
• Implemented enterprise-grade data governance and access control frameworks ensuring compliance lineage visibility and trust in analytics.

Programming and Software Engineering

• Strong proficiency in Python and SQL for data processing automation and API integration.
• Expertise in object-oriented programming (OOP) and design patterns in Python.
• Deep understanding of algorithmic complexity (Big O) and code performance optimization.
• Familiarity with parallel and distributed computing frameworks (Spark Dask Ray).
• Skilled with version control (Git) and CI/CD tools (GitLab Jenkins CircleCI).
• Proficient in software engineering best practices: testing (pytest/unittest) documentation type hinting and linting.
• Ability to debug profile and optimize large-scale data workflows.
• Project Experience:
• Developed performant maintainable Python-based data frameworks automated ETL systems and optimized code for distributed workloads.

AI/ML Pipeline Enablement

• Collaboration with data scientists on feature engineering data preparation and model deployment.
• Knowledge of ML orchestration and experiment tracking (MLflow Kubeflow).
• Familiarity with feature stores and data lineage for ML.
• Integration of batch and streaming data pipelines for real-time inference.
• Hands-on experience with analytics and visualization tools (Tableau Power BI).
• Project Experience:
• Built and maintained ML-ready data pipelines and infrastructure supporting training experimentation and real-time inference.

Leadership and Collaboration

• Mentoring and guiding junior engineers in data design coding standards and performance optimization.
• Leading cross-functional projects with data scientists analysts and business partners.
• Promoting best practices for data engineering and governance within the organization.
• Effective stakeholder communication documentation and Agile project management.
• Ability to conduct technical reviews and enforce design and scalability standards.
• Project Experience:
• Provided technical leadership for cross-domain data initiatives fostering best practices in data engineering and enabling scalable maintainable systems.

Additional Skills

• DevOps/DataOps: Infrastructure as code Docker/Kubernetes automated deployment of data infrastructure.
• Testing & CI/CD: Git-based workflows automated integration testing and continuous delivery for data pipelines.
• Performance & Cost Optimization: Tuning query execution pipeline efficiency and resource utilization.
• Automation: Building self-healing data pipelines with retry logic monitoring and alerting.
• Documentation: Strong communication of technical architecture using tools like Lucidchart PlantUML .