Data Engineer

Bixal•Fairfax, VA

3d•$130,000 - $135,000•Remote

About The Position

We are seeking a skilled and motivated Data Engineer to join our dynamic team as we continue to build and optimize data pipelines using PySpark, Databricks, AWS, and other cutting-edge technologies. The successful candidate will bring deep expertise in PySpark, Databricks, AWS, Kafka/streaming architectures, and infrastructure as code, and will be responsible for platform reliability, scalability, and evolution across the program. This is a full-time position contingent on contract award by our client, with a defined performance period of one year with two one-year option periods. This role offers you a unique opportunity to make a meaningful impact on a project that aligns with Bixal’s mission of delivering innovative, human-centered solutions. While the role has a fixed duration, we are committed to transparency and collaboration, keeping you informed about contract updates and new opportunities. At Bixal, we support your professional journey, ensuring your experience reflects our inclusive, purpose-driven culture and prepares you for future success. This role can work remotely from anywhere in the USA. You must be legally authorized to work in the US. Bixal does not provide visa sponsorship.

Requirements

Bachelor’s degree.
Minimum of 5 years of experience in Data Engineering.
Demonstrated experience serving as a technical SME and leading cross-functional engineering initiatives.
Proficiency in Python for data engineering tasks at scale.
Deep expertise with Databricks Intelligence Platform (Notebooks, Lakeflow Jobs, Unity Catalog, Delta Share) and the ability to define platform standards and best practices for the team.
Experience with AWS services such as S3 and IAM roles and policies.
Strong knowledge of data pipeline design and implementation, including data transformation techniques, data modeling, data storage optimization, and data security best practices.
Proficiency in using version control systems like Git for managing code repositories and collaborating with team members on Agile projects.
Familiarity with Terraform or other infrastructure as code tools for automating infrastructure deployment and configuration management.
Experience working on Linux environments for data engineering projects, including accessing containers remotely, installing packages, managing files, services, and processes.
Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases.
Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.
Strong problem-solving skills, ability to work independently and as part of a team, and excellent verbal and written communication skills.
Comfortable working in a highly collaborative environment with strong attention to detail and a commitment to delivering high-quality software.
Ability to obtain and maintain a Public Trust security clearance.

Nice To Haves

Bachelor’s degree in a technology field.
Familiarity with cloud computing concepts, particularly as they apply to data engineering, is a plus.
Experience working with other data frameworks such as Apache Hive, Apache Hadoop, or Apache Spark is a plus.
Federal consulting experience
Databricks certifications (Associate Data Engineer or Professional Data Engineer).
Experience with business intelligence tools (e.g., QuickSight, Power BI) for building data observability or usage reporting.

Responsibilities

Lead the design and implementation of complex data ingestion pipelines using PySpark, Databricks Intelligence Platform, and AWS services (S3, IAM roles and policies), including Kafka/streaming architectures, schema evolution, and Delta Share, ensuring alignment with enterprise architecture standards.
Serve as a subject matter expert (SME) for data engineering, providing technical guidance and mentorship to engineers across the team and representing data engineering in cross-functional discussions.
Take ownership of platform reliability and performance, proactively identifying optimization opportunities across data ingestion, transformation, and storage layers. Troubleshoot complex pipeline failures across multi-system dependencies.
Lead cross-functional technical initiatives, collaborating with data scientists, analysts, DevOps engineers, client teams (upstream data providers and downstream consumers), and stakeholders to deliver integrated solutions that meet program objectives.
Contribute to the data platform technical vision and architecture, making strategic decisions on tooling, frameworks, and design patterns that shape the program's long-term data engineering direction.
Define and enforce engineering standards, including code quality, testing practices, CI/CD processes, and infrastructure as code patterns using Terraform.
Evaluation of new technologies and approaches, presenting proof of concepts and technical roadmaps to leadership.
Author and maintain architectural documentation, technical decision records, and platform runbooks that enable team autonomy and operational excellence.
Other relevant duties as assigned and qualified/trained to perform