Urgent role-Databricks Engineer[Hybrid]-[education Background]

Position: Databricks Engineer Location: Adelphi, Maryland Visa: Any Visa except CPT, TN and GC (PP number is mandatory to fetch I94) Interview: webcam interview Responsibilities: 1. 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. 2. 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. 3. 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. 4. 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. 5. 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. 6. 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. 7. 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. 8. 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. Apply tot his job