Databricks Engineer

Since 1996, NFF has designed, architected, and delivered IT network and security solutions to many state, and local government agencies, K-20 educational institutions, federal agencies, and large enterprise businesses across the mid-Atlantic. NFF is a technology services and solutions provider, specializing in next-generation IT infrastructure including networks, data centers, cloud migrations, IT security, collaboration and mobility, and full/part-time staff augmentation services.   Our solutions, professional services and IT staffing portfolio are centered around building more resilient, secure, adaptive, and intelligent IT infrastructure and include comprehensive assessment, architecture, design, integration and installation services, and ongoing performance management services though our Network Operations Center (NOC).  

NFF is a Cisco Gold Partner with a Customer Experience Specialization and was a “Cisco Top-5 Mid-Atlantic SLED Partner” in 2019, 2020 and 2022. NFF has maintained Cisco Gold Partnership since 2008, is the only Cisco Gold Partner headquartered in the District of Columbia. In addition to Cisco, NFF has key partnerships with many manufacturers and IT solution providers including, Rapid7, Arctic Wolf, VMware, NetApp and Splunk.

NFF is a District of Columbia (DC) Certified Business Enterprise (CBE) and a SBA Certified Small Business with headquarters in downtown Washington, DC. Our dedication to quality is reflected in our accomplishment of being awarded multiple ISO 9001:2015 certifications.

Duties and Responsibilities:

• Design, implement, and optimize end-to-end data pipelines on Databricks, following the Medallion Architecture principles.
• Build robust and scalable ETL/ELT pipelines using Apache Spark and Delta Lake to transform raw (bronze) data into trusted curated (silver) and analytics-ready (gold) data layers.
• Operationalize Databricks Workflows for orchestration, dependency management, and pipeline automation.
• Apply schema evolution and data versioning to support agile data development.
• Connect and ingest data from enterprise systems such as PeopleSoft, D2L, and Salesforce using APIs, JDBC, or other integration frameworks.
• Implement connectors and ingestion frameworks that accommodate structured, semi-structured, and unstructured data.
• Design standardized data ingestion processes with automated error handling, retries, and alerting.
• Develop data quality checks, validation rules, and anomaly detection mechanisms to ensure data integrity across all layers.
• Integrate monitoring and observability tools (e.g., Databricks metrics, Grafana) to track ETL performance, latency, and failures.
• Implement Unity Catalog or equivalent tools for centralized metadata management, data lineage, and governance policy enforcement.
• Enforce data security best practices including row-level security, encryption at rest/in transit, and fine-grained access control via Unity Catalog.
• Design and implement data masking, tokenization, and anonymization for compliance with privacy regulations (e.g., GDPR, FERPA).
• Work with security teams to audit and certify compliance controls.
• Enable data scientists by delivering high-quality, feature-rich data sets for model training and inference.
• Support AIOps/MLOps lifecycle workflows using MLflow for experiment tracking, model registry, and deployment within Databricks
• Collaborate with AI/ML teams to create reusable feature stores and training pipelines.
• Architect and manage data lakes on Azure Data Lake Storage (ADLS) or Amazon S3, and design ingestion pipelines to feed the bronze layer.
• Build data marts and warehousing solutions using platforms like Databricks.
• Optimize data storage and access patterns for performance and cost-efficiency.
• Maintain technical documentation, architecture diagrams, data dictionaries, and runbooks for all pipelines and components.
• Provide training and enablement sessions to internal stakeholders on the Databricks platform, Medallion Architecture, and data governance practices.
• Conduct code reviews and promote reusable patterns and frameworks across teams.
• Submit a weekly schedule of hours worked and progress reports outlining completed tasks, upcoming plans, and blockers.
• Track deliverables against roadmap milestones and communicate risks or dependencies.

Qualifications:

• Hands-on experience with Databricks, Delta Lake, and Apache Spark for large-scale data engineering.
• Deep understanding of ELT pipeline development, orchestration, and monitoring in cloud-native environments.
• Experience implementing Medallion Architecture (Bronze/Silver/Gold) and working with data versioning and schema enforcement in enterprise grade environments.
• Strong proficiency in SQL, Python, or Scala for data transformations and workflow logic.
• Proven experience integrating enterprise platforms (e.g., PeopleSoft, Salesforce, D2L) into centralized data platforms.
• Familiarity with data governance, lineage tracking, and metadata management tools.

Preferred Qualifications:

• Experience with Databricks Unity Catalog for metadata management and access control.
• Experience deploying ML models at scale using MLFlow or similar MLOps tools.
• Familiarity with cloud platforms like Azure or AWS, including storage, security, and networking aspects.
• Knowledge of data warehouse design and star/snowflake schema modeling.

NFF offers a competitive salary, comprehensive benefits and flexible paid time off options, for eligible employees:

• Medical, Dental and Vision, Health Savings Account, Flexible Spending Account
• STD, LTD, Supplemental life insurance and ADD&D
• Comprehensive 401k plan
• Paid Time Off

NFF is an Equal Opportunity Employer.

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