Data Platform Lead
Apply NowCompany: Cambridge Associates
Location: Boston, MA 02115
Description:
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.
Key Responsibilities
Strategic Leadership and Domain Expertise
Develop and execute a strategic roadmap for the data platform, aligning it with organizational goals, business growth and the evolving needs of the financial services domain.
Lead the development of a centralized data platform that supports the unique needs of the asset and wealth management domain, including portfolio and fund data management, client reporting, and regulatory compliance.
Collaborate with business stakeholders to understand operational workflows, dependencies, and data requirements across functions such as investment management, client servicing, compliance, and risk management.
Act as a thought leader in the financial services domain, driving innovation and best practices in data engineering to support business objectives.
Platform Design and Architecture
Define and implement the overall architecture for the enterprise data platform, ensuring scalability, reliability, and cost-efficiency.
Design and implement a centralized data warehouse that integrates data from multiple operational systems, enabling seamless access to accurate and consistent data for analytics and reporting.
Build a data mesh architecture to enable decentralized data ownership while maintaining centralized governance and data quality.
Leverage tools (like Snowflake, Databricks, or AWS S3) to build a cloud-native data lake and data warehouse solution that supports large-scale data storage and analytics.
Integrate advanced AI-driven data quality and monitoring tools to ensure data accuracy, reliability, and consistency across the organization.
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.
Team Leadership and Collaboration
Lead and mentor a team of data engineers, fostering a culture of innovation, collaboration, and continuous learning.
Collaborate with data scientists, analysts, and business stakeholders to understand data requirements and deliver tailored solutions.
Act as a liaison between technical teams and business units, translating complex technical or business concepts into actionable insights.
Innovation and Continuous Improvement
Stay updated on emerging trends and technologies in data engineering, cloud computing, and the financial services domain.
Evaluate and recommend new features, tools, frameworks, and methodologies to enhance the platform's capabilities.
Drive continuous improvement initiatives to optimize platform performance, reduce costs, and improve user experience.
Qualifications and Experience
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.
Why Join Us?
Be part of a forward-thinking organization that values data-driven decision-making and innovation.
Work with cutting-edge technologies and contribute to building a world-class data platform tailored to the financial services domain.
Collaborate with a diverse and talented team in a dynamic and supportive environment.
Enjoy opportunities for professional growth and development.
Certifications
Relevant certifications such as Certified Data Management Professional (CDMP) or DM-BOK is a plus.
Want to learn more?
Click HERE to learn more about how Cambridge Associates lives our firm values every day. (https://www.cambridgeassociates.com/about-us/firm-values/)
Click HERE to learn more about Cambridge Associates invests in diversity and inclusion. (https://www.cambridgeassociates.com/about-us/diversity-inclusion/)
Click HERE to learn more about our commitment to Corporate Social Responsibility. (https://www.cambridgeassociates.com/about-us/corporate-social-responsibility/)
EQUAL OPPORTUNITY EMPLOYMENT
The firm is committed to the concept and practice of equal employment opportunity and will not discriminate against any employee or applicant on the basis of race, color, religion, age, sex, national origin, sexual orientation, gender identity, disability, or veteran status. It is expected that all employees will follow a similar policy toward their co-workers.
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.
Key Responsibilities
Strategic Leadership and Domain Expertise
Develop and execute a strategic roadmap for the data platform, aligning it with organizational goals, business growth and the evolving needs of the financial services domain.
Lead the development of a centralized data platform that supports the unique needs of the asset and wealth management domain, including portfolio and fund data management, client reporting, and regulatory compliance.
Collaborate with business stakeholders to understand operational workflows, dependencies, and data requirements across functions such as investment management, client servicing, compliance, and risk management.
Act as a thought leader in the financial services domain, driving innovation and best practices in data engineering to support business objectives.
Platform Design and Architecture
Define and implement the overall architecture for the enterprise data platform, ensuring scalability, reliability, and cost-efficiency.
Design and implement a centralized data warehouse that integrates data from multiple operational systems, enabling seamless access to accurate and consistent data for analytics and reporting.
Build a data mesh architecture to enable decentralized data ownership while maintaining centralized governance and data quality.
Leverage tools (like Snowflake, Databricks, or AWS S3) to build a cloud-native data lake and data warehouse solution that supports large-scale data storage and analytics.
Integrate advanced AI-driven data quality and monitoring tools to ensure data accuracy, reliability, and consistency across the organization.
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.
Team Leadership and Collaboration
Lead and mentor a team of data engineers, fostering a culture of innovation, collaboration, and continuous learning.
Collaborate with data scientists, analysts, and business stakeholders to understand data requirements and deliver tailored solutions.
Act as a liaison between technical teams and business units, translating complex technical or business concepts into actionable insights.
Innovation and Continuous Improvement
Stay updated on emerging trends and technologies in data engineering, cloud computing, and the financial services domain.
Evaluate and recommend new features, tools, frameworks, and methodologies to enhance the platform's capabilities.
Drive continuous improvement initiatives to optimize platform performance, reduce costs, and improve user experience.
Qualifications and Experience
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.
Why Join Us?
Be part of a forward-thinking organization that values data-driven decision-making and innovation.
Work with cutting-edge technologies and contribute to building a world-class data platform tailored to the financial services domain.
Collaborate with a diverse and talented team in a dynamic and supportive environment.
Enjoy opportunities for professional growth and development.
Certifications
Relevant certifications such as Certified Data Management Professional (CDMP) or DM-BOK is a plus.
Want to learn more?
Click HERE to learn more about how Cambridge Associates lives our firm values every day. (https://www.cambridgeassociates.com/about-us/firm-values/)
Click HERE to learn more about Cambridge Associates invests in diversity and inclusion. (https://www.cambridgeassociates.com/about-us/diversity-inclusion/)
Click HERE to learn more about our commitment to Corporate Social Responsibility. (https://www.cambridgeassociates.com/about-us/corporate-social-responsibility/)
EQUAL OPPORTUNITY EMPLOYMENT
The firm is committed to the concept and practice of equal employment opportunity and will not discriminate against any employee or applicant on the basis of race, color, religion, age, sex, national origin, sexual orientation, gender identity, disability, or veteran status. It is expected that all employees will follow a similar policy toward their co-workers.