Databricks Engineer Job at WINTrio (Leesburg)

Job Description

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.

Job Responsibility

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

Requirements

Hands-on experience with Databricks, Delta Lake, and Apache Spark
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

Nice to have

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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Databricks Engineer

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