Vice President, Data Engineering & Data Products

Ares Management is seeking a Vice President, Data Engineering & Data Products is responsible for leading the enterprise data engineering organization responsible for transforming raw data into trusted, consumable, and business-ready data products. This role owns the design, development, and operation of scalable data pipelines, transformation frameworks, data models, and curated data products built on the Databricks Lakehouse platform.

The successful candidate will partner closely with Data Ingestion & Integration, Data Distribution Services, Data Governance, Master Data Management, Enterprise Architecture, and business domain teams to deliver trusted and reusable enterprise data products.

Reports to: Principal, Head of Data Platforms

Supervises: n/a

·       Define and execute the enterprise Data Engineering and Data Products strategy.

·       Establish engineering standards, frameworks, and best practices for data transformation and product development.

·       Build and lead high-performing teams of data engineers, platform engineers, and data product developers.

·       Drive the transition from project-based data delivery to product-centric data management.

·       Partner with business and technology leaders to prioritize strategic data products and capabilities.

·       Define engineering KPIs, quality metrics, SLAs, and operational objectives.

·       Lead technology investment decisions and engineering modernization initiatives.

·       Own enterprise ETL/ELT standards and transformation frameworks.

·       Design and deliver scalable pipelines that transform raw source data into business-ready datasets.

·       Lead development of reusable transformation patterns utilizing Databricks and Spark.

·       Implement complex business logic, aggregations, calculations, and derived metrics.

·       Enable standardized enrichment processes leveraging internal and external datasets.

·       Optimize data processing performance, scalability, and cost efficiency.

·       Ensure consistency of data transformations across business domains.

·       Partner directly with stakeholders across Investment Operations, Finance, Compliance, Investor Relations, Risk, and Investment teams.

·       Translate complex business requirements into scalable technical solutions.

·       Drive successful delivery of strategic data initiatives and enhancements.

·       Implement and support data quality controls, lineage, metadata, and governance capabilities.

·       Contribute to data security, privacy, and compliance requirements.

·       Establish technical standards and engineering best practices.

·       Lead technical workstreams and complex engineering initiatives.

·       Mentor junior team members and provide technical guidance.

·       Participate in architecture discussions and technology evaluations.

·       Collaborate with leadership on platform roadmap and prioritization efforts.

• 15+ years of experience in Data Engineering, Data Warehousing, Analytics Engineering, or Data Platform Engineering.
• 8+ years of leadership experience managing large engineering organizations.
• Proven experience building enterprise-scale data platforms and engineering capabilities.
• Deep experience designing and delivering business-facing data products.
• Strong experience within financial services, asset management, banking, insurance, or other regulated industries preferred.

·       Extensive experience designing and implementing enterprise-scale ETL/ELT solutions utilizing Databricks, Apache Spark, Delta Lake, Python, SQL, and Azure Data Lake Storage to support high-volume analytical, operational, and regulatory workloads.

·       Deep expertise implementing and governing Databricks Medallion Architecture (Bronze, Silver, Gold) with standardized processes for data refinement, certification, promotion, and lifecycle management.

·       Proven experience transforming complex source data into canonical, normalized, dimensional, and domain-oriented data models, enabling consistent enterprise-wide consumption and interoperability.

·       Strong expertise building data enrichment frameworks that combine transactional, master, reference, market, and third-party datasets to create trusted and reusable business data products.

·       Extensive experience implementing bi-temporal data models, temporal versioning, SCD patterns, effective-dating, and historical tracking frameworks supporting audit, regulatory, and point-in-time reporting requirements.

·       Strong experience designing enterprise-grade data quality, reconciliation, exception handling, error management, and reprocessing frameworks, including automated validation, data certification, root-cause analysis, and operational remediation workflows.

·       Deep expertise implementing observability capabilities utilizing Databricks Lakehouse Monitoring, Delta Live Tables expectations, Unity Catalog Lineage, Databricks System Tables, Azure Monitor, Log Analytics, Application Insights, dashboards, telemetry, alerting, and operational analytics to ensure reliability, transparency, and SLA compliance across all data products.