Databricks Engineer
hace 11 días
Baltimore
Job DescriptionThis posting is for a pending award. We are seeking a Databricks Engineer to design, build, and operate a Data & AI platform with a strong foundation in the Medallion Architecture (raw/bronze, curated/silver, and mart/gold layers). This platform will orchestrate complex data workflows and scalable ELT pipelines to integrate data from enterprise systems such as PeopleSoft, D2L, and Salesforce, delivering high-quality, governed data for machine learning, AI/BI, and analytics at scale. You will play a critical role in engineering the infrastructure and workflows that enable seamless data flow across the enterprise, ensure operational excellence, and provide the backbone for strategic decision-making, predictive modeling, and innovation. Responsibilities: • Data & AI Platform Engineering (Databricks-Centric):, • 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., • Platform Integration & Data Ingestion:, • 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., • Data Quality, Monitoring, and Governance:, • 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., • Security, Privacy, and Compliance:, • 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., • AI/ML-Ready Data Foundation:, • 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., • Cloud Data Architecture and Storage:, • 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., • Documentation & Enablement:, • 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., • Reporting and Accountability:, • 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. Required 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., • Competitive compensation, • Health benefits including Medical, Dental and Vision, • Vacation and Personal Days, • 401K, • Employee Assistance Plan filFUxTjTR