Director, Data Integration and Interoperability
University of Maryland Global CampusAbout the role
Location: Stateside Remote
The Director, Data Integration and Interoperability will lead enterprise-wide data ingestion, transformation, and delivery initiatives in a Databricks centered environment. This role is critical in designing and operating robust data integration pipelines across internal and external sources, enabling access via APIs and lake federation, and other mechanisms ensuring that data is FAIR (Findable, Accessible, Interoperable, Reusable).
This position will design and oversee ELT and Lake Federation processes, as well as interoperability with MDM, data governance, DevOps, MLOps, and AIOps environments. In addition, it will be responsible for enabling dimensional data modeling to support scalable analytics and business intelligence solutions. This position requires both technical leadership and strong collaboration with engineering, data governance, analytics, and business teams.
Duties and Responsibilities:
Data Ingestion and Integration
Design and oversee scalable ingestion pipelines using Databricks pipelines, Lakeflow Declarative Pipelines, Delta Lake from diverse internal and external data sources.
Utilize tools like Databricks Auto Loader, Python, Kafka, REST APIs, SQL, and cloud-native connectors for real-time and batch data flows.
Establish standardized ingestion and orchestration patterns based on data provider contracts ensuring SLAs, quality, observability, and operational reliability.
Data Delivery and Federation
Ensure delivery of data to consumer apps and systems maintaining SLAs specified in data contracts.
Implement lake federation strategies to unify access across cloud platforms and on-premises systems.
Lead implementation and operation of API Store, managing delivery of curated and governed data sets via REST APIs, SQL endpoints, and federated access layers with fine grained access control.
Ensure scalable, secure, and performant data access for downstream analytics, reporting, and machine learning.
Interoperability with Enterprise Data Platform components
Implement data interoperability between the data Lakehouse and the Master Data Management (MDM) system (i.e., Profisee) in support of federated data stewardship and quality processes.
Collaborate with data governance and security teams to ensure proper metadata management, lineage tracking, and compliance with data access, archival, and regulatory policies and regulations (e.g., FERPA, GDPR, PCA, etc.) managed in a Purview environment.
Enable interoperability with MLOps and AIOps platforms to streamline model deployment, monitoring, and lifecycle management.
Dimensional Modeling and Analytics Support
Guide data engineering teams in designing and implementing dimensional models to support all enterprise reporting and analytics needs including AI/BI, BI, reverse ETL, lake federation, etc.
Ensure data models align with business definitions, support high-performance queries, and integrate cleanly with semantic layers, data quality, and data consumer tools and processes.
Partner with analytics teams to identify modeling needs and implement scalable, reusable data structures.
Metadata Management and Governance
Lead the implementation and automation of metadata management practices across all data pipelines and assets.
Collaborate on development of metadata stores in Unity Catalog to ensure consistent documentation, lineage tracking, and impact analysis.
Ensure technical, business, and operational metadata are captured and maintained for all datasets and records.
Collaborate with data governance teams and stakeholders to enforce metadata standards, naming conventions, and classification policies within the enterprise data platform and across enterprise systems.
Support discovery, reuse, and transparency of data assets through well-governed metadata practices.
Lead implementation of observability tools and reports.
Leadership and Strategy
Build and lead a team of data integration engineers and architects; provide mentoring, technical guidance, and career development.
Define roadmaps and execution plans for data interoperability and integration capabilities.
Manage project delivery timelines, budgets, and cross-functional dependencies.
Engage with business and technical stakeholders across the insti
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