WINTrio LLC

Posted 2 weeks ago

WINTrio LLC

Databricks Engineer

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.

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.
  • 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.

Application Instructions

Please submit your resume at Capture@wintrio.com.

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