Head of Data Platform Engineering, MD

Role Overview

The Head of Data Platform Engineering will lead the design, build, and operation of a modern, fully cloud‑native data platform supporting enterprise‑wide analytics, data science, and AI initiatives. This role is ideal for a seasoned engineering leader with a deep background in large‑scale data systems, distributed computing, and modern cloud architectures especially Azure. The ideal candidate combines technical excellence, strategic vision, and the ability to scale teams, processes, and platforms that enable data‑driven decision making.

Charles River Development empowers investment firms worldwide with a cloud‑based platform that streamlines and automates the investment process across asset classes. As part of State Street Alpha®, we bring together data, technology, and services to help clients make confident decisions and scale with agility. Our open ecosystem and global support model enable investment professionals—from portfolio managers to private markets specialists—to navigate complexity and drive innovation.

We thrive on collaboration, continuous improvement, and a shared commitment to shaping the future of financial technology.

• Define and execute the long‑term vision and architecture for a unified, cloud‑native data platform supporting data ingestion, transformation, storage, governance, and access at scale.
• Lead the engineering teams building core components of the data platform: data lakehouse, real‑time streaming, metadata catalog, observability framework, and security layers.
• Drive selection and adoption of technologies (e.g., Snowflake, Databricks, Big Query, Kafka, Airflow, dbt, Delta Lake, Iceberg, or similar) aligned with business requirements and cloud‑native best practices.
• Establish foundations for high‑quality data engineering: CI/CD pipelines, IaC (Terraform, Cloud Formation), automated testing, schema versioning, and data quality monitoring.
• Partner with Product, Analytics, and ML teams to define data contracts, SLAs, and governance standards that ensure reliability, consistency, and compliance.
• Build, mentor, and scale a multi‑disciplinary engineering organization — attracting top data engineers, infrastructure specialists, and SREs.
• Represent the data platform strategy to executive leadership, ensuring alignment with enterprise technology and business goals.

• 20+ years of software engineering experience, including at least 10 years in data infrastructure or platform leadership roles.
• Proven record of leading large‑scale cloud migrations or greenfield cloud‑native data platform builds.
• Deep understanding of distributed data systems, including storage (object stores, data warehouses, lake houses), compute (Spark, Flink, Presto), and event streaming (Kafka, Kinesis, Pub/Sub).
• Hands‑on background in modern Dev Ops and Data Ops practices: CI/CD, container orchestration (Kubernetes), infrastructure automation, and observability.
• Familiarity with security and compliance frameworks (GDPR, SOC 2, HIPAA, etc.).
• Strong people‑leadership skills: team building, coaching, and creating high‑performance cultures across geographies.
• Experience partnering with analytics, ML, and enterprise application teams to deliver end‑to‑end data solutions.
• Exceptional communication skills with the ability to articulate complex technical concepts to executives.

• Prior experience architecting platforms using AWS (Glue, S3, EMR, Redshift) or Azure Data Services (Synapse, Data Factory, ADLS Gen2) or GCP (Big Query, Dataflow, Pub/Sub).
• Familiarity with modern governance frameworks (Unity Catalog, Collibra, Alation).
• Exposure to AI/ML enablement (feature stores, model registries, data versioning).
• Engineering degree in Computer Science, Engineering, or related field.

• Delivery of a scalable, reliable, and secure data platform used by analytics, ML, and operational stakeholders across the company.
• Reduction in data latency, increase in data quality, and measurable platform adoption.
• Growth and retention of high‑performing engineering talent.
• Clear roadmap execution, cost efficiency, and alignment with business data initiatives.