[Remote] Data Engineer AI/BI VoS

Note: The job is a remote job and is open to candidates in USA. The hiring company is confidential and is seeking a Data Intelligence Engineer to build and operate Databricks-based data and AI capabilities. The role focuses on developing scalable ELT pipelines, AI/BI dashboards, data governance, secure cloud data architecture, and AI/ML-ready data foundations integrated with enterprise systems.

Responsibilities

• Build and scale Databricks AI/BI solutions end to end, combing governed semantic models, SQL, and performance optimized query layers
• Develop and operationalize Databricks Genie experiences by curating datasets, metadata, and prompts for natural language, self-service analytics
• Design and deliver Databricks dashboards and visual products that translate data into clear actionable insights
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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

Skills

• Hands-on experience with Databricks (Delta Lake, Apache Spark) and building AI/BI solutions, including dashboards, semantic models, and Genie based natural language analytics
• 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
• 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

Company Overview