Enterprise Data Platform Engineering

Arrowstreet Capital•Boston, MA

About The Position

We seek an experienced and innovative Sr Engineer to architect, design, optimize, and implement a cutting-edge data platform in collaboration with our Business and Client teams. You will leverage your expertise in developing, enhancing, and managing large-scale data systems to drive strategic initiatives and elevate the capabilities of our data platforms. This role will spearhead the strategic planning, organizational management, and execution of our data engineering roadmap, collaborating with cross-functional teams to deliver a unified customer view and enable data-driven decision-making. Your primary responsibility will be to create an enterprise-wide data platform that streamlines the entire data lifecycle - from ingestion to retrieval - ensuring seamless integration, scalability, and high-quality data throughout. You will maintain rigorous standards for security, governance, and data quality to ensure trustworthiness and compliance. You will partner with cross-functional groups to transform our existing data infrastructure into a modern, cloud-native solution that fuels business insights and drives value. Your focus will include migrating core business processes across diverse systems and locations to a unified data layer, enabling technical teams to publish data and end-users to access it seamlessly.

Requirements

10+ years of experience in technology leadership roles within a Business domain with ideally some experience in the Investment Industry
Deep expertise in designing and building Data Systems.
Proven track record in producing-quality software designs.
Strong knowledge of an object-oriented programming language (Python, C++, Java, C#, Rust), software testing methodologies and software architecture.
Proven experience managing engineering teams and delivering enterprise-grade platforms in a complex, regulated environment.
Hands-on experience with cloud platforms (AWS, Azure, or GCP), microservices, APIs, and data pipelines.
Experience implementing Reporting and Analytical platforms, leveraging modern technologies to ingest data across disparate systems
Experience working with GenAI technologies specific to enriching data platforms (RAG, MCP).
Demonstrated expertise in SQL, Spark, Pandas, or similar technologies.
Solid understanding of data governance, data modeling, and master data management practices aligning to implementing large scale data platforms.
Exceptional communication skills and the ability to build strong relationships as well as credibility.
Excellent leadership and management skills, with the ability to mentor and develop high-performing teams.
Expertise in budget control and vendor management preferred.
Strong strategic planning and execution abilities, with a focus on aligning data.
Bachelor’s degree in Computer Science, Software Engineering or a related discipline.

Responsibilities

Technology Strategy & Architecture: Define and execute the technical roadmap for the platform
Oversee and contribute to building features for data discovery, lineage, governance, ingestion operation, cost management, performance, etc.
Work with Technical and Business stakeholders deliver analytical and reporting platforms that scale to meet current and future business requirements.
Leadership and Collaboration Collaborate closely with business stakeholders to understand their data needs, translate business objectives into scalable and efficient data engineering solutions.
Partner with IT operations and cloud security teams to ensure platform solutions are secure, scalable, and reliable.
Lead, mentor and manage a team of Data Engineers.
Foster strong partnerships with technology vendors.
Negotiate and manage technical contracts to support the organization's needs and ensure cost-effective solutions.
Delivery and Operational Excellence Lead and assist the team in investigating potential improvements achieved by adopting new data technologies and frameworks.
Achieve high quality metrics through automated testing, robust operational tooling, and highly-available cloud architecture.
Implement processes to ensure the quality and availability of data and build functionality to improve the overall robustness of our data platform.
Design verification plans and implement unit tests to validate the correctness and performance of the solutions.
Design a reliable production support process and, when required, assist in supporting and troubleshooting production issues.
Automate all aspects of the data lifecycle.
Optimize existing data systems and analytics applications.