What are the responsibilities and job description for the Databricks Engineer position at Maayee, Inc.?
Responsibilities
- Data & AI Platform Engineering (Databricks-Centric):
- Design, implement, and optimize endtoend 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 analyticsready (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, semistructured, 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. UMGC RFP #92248 – IT Staff Augmentation Services.
- 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.
- Handson experience with Databricks, Delta Lake, and Apache Spark for largescale data engineering. Deep understanding of ELT pipeline development, orchestration, and monitoring in cloudnative 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.
- 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.
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