Databricks Engineer Jobs in Windsor Mill, MD (NOW HIRING)

Role: Databricks Engineer

Location: Baltimore, Maryland

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.

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.