Senior Databricks Data Engineer

May 13, 2026

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Overview: We are seeking a Senior Databricks Data 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 senior technical role in guiding engineering standards, reusable patterns, platform reliability, and production readiness across the Data & AI platform. 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: 1. Data & AI Platform Engineering (Databricks-Centric): Provide senior-level technical leadership in the design, optimization, and standardization of Databricks engineering patterns across the Data & AI platform. 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. This will be a Remote role Operationalize Databricks Workflows for orchestration, dependency management, and pipeline automation. Apply schema evolution and data versioning to support agile data development. 2. Platform Integration & Data Ingestion: Lead the design of reusable ingestion frameworks and integration patterns that support scalable, reliable, and governed data onboarding across enterprise systems. 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. 3. Data Quality, Monitoring, and Governance: Establish senior-level data quality, observability, and governance practices to improve trust, reliability, lineage, and operational transparency across the platform. 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. 4. Security, Privacy, and Compliance: Provide senior technical guidance on secure data engineering practices, access-control patterns, and compliance implementation across Databricks and related cloud environments. 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. 5. AI/ML-Ready Data Foundation: Partner with data science and AI/ML teams to shape reusable, production-ready data engineering patterns that support scalable model development, deployment, and monitoring. 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. 6. Cloud Data Architecture and Storage: Contribute to senior-level cloud data architecture decisions related to data lake design, storage optimization, compute efficiency, security, and cost management. 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. 7. Documentation & Enablement: Mentor engineers and promote senior-level engineering standards through documentation, code reviews, reusable frameworks, and knowledge-sharing practices. 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. 8. Reporting and Accountability: Take ownership of complex data engineering deliverables, production issues, technical risks, and cross-team dependencies requiring senior-level judgment and coordination. 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:<ul><li>Senior-level hands-on experience designing, building, optimizing, and supporting production-grade Databricks data engineering solutions in enterprise environments.</li><li>5+ years of hands-on experience with Databricks, Delta Lake, and Apache Spark for large-scale data engineering.</li><li>Deep understanding of ELT pipeline development, orchestration, and monitoring in cloud-native environments.</li><li>Experience implementing Medallion Architecture (Bronze/Silver/Gold) and working with data versioning and schema enforcement in enterprise grade environments.</li><li>Strong proficiency in SQL, Python, or Scala for data transformations and workflow logic.</li><li>Proven experience integrating enterprise platforms (e.g., PeopleSoft, Salesforce, D2L) into centralized data platforms.</li><li>Familiarity with data governance, lineage tracking, and metadata management tools.</li></ul>Preferred Qualifications:<ul><li>Experience serving as a senior engineer, technical lead, or mentor on enterprise data platform or Databricks implementation initiatives.</li><li>Prior UMGC or USM experience preferred.</li><li>Experience with Databricks Unity Catalog for metadata management and access control.</li><li>Experience deploying ML models at scale using MLFlow or similar MLOps tools.</li><li>Familiarity with cloud platforms like Azure or AWS, including storage, security, and networking aspects.</li><li>Knowledge of data warehouse design and star/snowflake schema modeling</li></ul>

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