Vice President, Data Engineering & Data Products

Ares Management Corporation•New York, NY

17h•$210,000 - $230,000

About The Position

Ares Management is seeking a Vice President, Data Engineering & Data Products to lead the enterprise data engineering organization, transforming raw data into trusted, consumable, business-ready data products. The 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 work closely with Data Ingestion & Integration, Data Distribution Services, Data Governance, Master Data Management, Enterprise Architecture, and business domain teams to deliver trusted, reusable enterprise data products. The role also partners with business and technology leaders to prioritize strategic data products, modernize engineering capabilities, and advance the platform roadmap. The position leads data engineers, platform engineers, and data product developers; establishes engineering standards and operational measures; and drives the shift from project-based data delivery to product-centric data management. It also supports data quality, lineage, metadata, governance, security, privacy, and compliance capabilities.

Requirements

Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field.
15+ years of experience in Data Engineering, Data Warehousing, Analytics Engineering, or Data Platform Engineering.
8+ years of leadership experience managing large engineering organizations.
Extensive experience designing and implementing enterprise-scale ETL/ELT solutions using Databricks, Apache Spark, Delta Lake, Python, SQL, and Azure Data Lake Storage for high-volume analytical, operational, and regulatory workloads.
Deep expertise implementing and governing Databricks Medallion Architecture (Bronze, Silver, Gold), including standardized data refinement, certification, promotion, and lifecycle management.
Proven experience transforming complex source data into canonical, normalized, dimensional, and domain-oriented data models for consistent enterprise-wide consumption and interoperability.
Strong expertise building data enrichment frameworks combining transactional, master, reference, market, and third-party datasets into trusted, reusable business data products.
Extensive experience implementing bi-temporal data models, temporal versioning, SCD patterns, effective-dating, and historical tracking frameworks for audit, regulatory, and point-in-time reporting.
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 using 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 support reliability, transparency, and SLA compliance.
Proven experience building enterprise-scale data platforms and engineering capabilities.
Deep experience designing and delivering business-facing data products.

Nice To Haves

Strong experience within financial services, asset management, banking, insurance, or other regulated industries.

Responsibilities

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 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 using Databricks and Spark.
Implement complex business logic, aggregations, calculations, and derived metrics.
Enable standardized enrichment processes using internal and external datasets.
Optimize data processing performance, scalability, and cost efficiency.
Ensure consistency of data transformations across business domains.
Partner with stakeholders across Investment Operations, Finance, Compliance, Investor Relations, Risk, and Investment teams.
Translate complex business requirements into scalable technical solutions.
Drive 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.
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.