JOB SUMMARY:

Cambridge Associates (“CA”) is a leading global investment firm. CA’s goal is to help endowments & foundations, pension plans, and ultra-high net worth private clients implement and manage custom investment portfolios that generate outperformance so that they can maximize their impact on the world. Cambridge Associates delivers a range of services, including outsourced CIO, non-discretionary portfolio management, and investment consulting.
Headquartered in Boston, Massachusetts, CA has offices in key markets in North America, the United Kingdom, Europe, Asia, and Oceania. Our worldwide teams ensure our clients benefit from decades of global presence, local expertise, and relationships with the top global investment managers across the world. For more information, please visit www.cambridgeassociates.com.
We are seeking a highly skilled and visionary Director, Data Engineering with deep expertise in designing, implementing, and managing modern data platforms tailored to the financial services domain, particularly in asset and wealth management space. This role will focus on building a scalable, secure, and high-performance data ecosystem that supports an enterprise data lake/warehouse solution, advanced analytics, operational workflows, data governance and decision-making processes.
The ideal candidate will have a strong financial services domain understanding and hands-on experience working with centralized enterprise-grade data warehousing solutions. They will lead the end-to-end design and implementation of the data platform, ensuring it meets the organization’s current and future needs while adhering to best practices and regulatory standards.

EDUCATION

• Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related field. A Master’s degree is preferred.

EXPERIENCE

• 10+ years of experience in data engineering, with at least 3-5 years in a leadership role.
• Proven expertise in designing and implementing data platforms using Snowflake (preferred), Databricks, and other modern data stack technologies.
• Extensive experience in the financial services domain, particularly in asset and wealth management, with a strong understanding of operational and reporting workflows and dependencies.
• Experience with commercial and open-source ETL tools, including:
• Informatica, Talend, Apache Airflow, dbt, AWS Glue, Fivetran, and Matillion.
• Real-time data streaming tools like Apache Kafka, Apache Flink, or Spark Streaming.
• Strong knowledge of foundational aspects of data engineering, including:
• Data security and encryption
• SSO configuration and management
• Handling NPI/PII data and ensuring compliance with regulatory standards
• Data versioning and referential integrity
• Data governance and metadata management
• Familiarity with DataOps practices to improve collaboration and reduce time-to-market for data products.
• Experience with cloud platforms (e.g., AWS, Azure, GCP) and their data services.

SKILLS AND COMPETENCIES

• Strong programming skills in Python, SQL, Scala or any other programming languages.
• Proficiency in data architecture and modeling, database design, and query optimization.
• Exceptional problem-solving and analytical skills, with a focus on delivering scalable and reliable solutions.
• Excellent communication and leadership skills, with the ability to engage and influence stakeholders at all levels (technical and non-technical).
• Familiarity with data visualization tools (e.g., Tableau, Power BI) and their integration with data platforms.

FUNCTIONAL RESPONSIBILITIES

• Data Ingestion and Integration:
• Build robust pipelines to ingest data from diverse sources, including data operations teams, portfolio management systems, CRM platforms, market data providers, and regulatory systems.
• Leverage ETL/ELT tools (such as Informatica, Talend, Apache Airflow, dbt, AWS Services, and Fivetran) to automate and optimize data workflows.
• Implement real-time data streaming solutions using open-source solutions (like Apache Kafka, Apache Flink, and Spark Streaming) to process millions of events per second.
• Data Modeling and Storage:
• Design and implement data models and data modelling design patterns optimized for flexibility, scalability, financial analytics, client reporting, and regulatory submissions.
• Manage structured, semi-structured, and unstructured data using storage solutions to optimize business outcomes and manage costs.
• Data Governance and Quality:
• Define and enforce data platform related governance policies, including metadata management, data lineage, and stewardship, with a focus on financial data accuracy, security, regulatory compliance and adherence to standards.
• Implement automated data validations and quality checks using commercially available tools (like informatica, Ataccama) or custom validation frameworks, reducing data errors and improving reliability.
• Ensure compliance with regulatory standards such as GDPR, CCPA, MiFID II, and SEC requirements, with a focus on managing sensitive data (e.g., NPI and PII) securely.
• Operational Workflow Integration:
• Collaborate with operations teams to understand dependencies between the centralized data warehouse and key workflows, such as client onboarding, trade processing, portfolio rebalancing, and reporting.
• Ensure the data platform supports operational efficiency by providing timely and accurate data for decision-making and reporting.

NON-FUNCTIONAL RESPONSIBILITIES

• Performance Optimization:
• Optimize query performance and storage costs through partitioning, clustering, and caching strategies.
• Monitor and tune the performance of data pipelines and workloads to meet SLAs.
• Scalability and Reliability:
• Design the platform to handle large-scale data volumes and high-concurrency workloads typical of financial services operations.
• Implement disaster recovery and high-availability strategies to ensure business continuity.
• Data Versioning and Referential Integrity:
• Establish processes for data versioning to track changes and support reproducibility.
• Enforce referential integrity checks to maintain consistency across related datasets.
• Configuration Management:
• Define and manage platform configurations, including resource allocation, SSO, user roles, and environment settings.