Senior Engineering Manager, Reporting Data Curation

Ridgeline•San Ramon, NY

1d•Hybrid

About The Position

This role is for an experienced engineering leader to build high-performing teams that deliver scalable, high-quality data products. The Senior Engineering Manager will shape data models and operational reporting systems that power critical decisions across complex financial workflows, leading teams in solving data curation and querying challenges at scale within a modern data intelligence platform. Specifically, this role leads Ridgeline’s Reporting Data Curation team within the Data Intelligence Platform, owning the core reporting and data-querying infrastructure for customer-facing reporting experiences and internal analytics. The manager will drive the design and delivery of scalable operational reporting, high-quality data models, and the evolution of a next-generation distributed query and reporting engine. The work involves transforming complex financial data into reliable, high-performance reporting systems to enable faster, more informed decision-making for investment managers. The engineering team utilizes cutting-edge technologies like Claude Code and Cursor in a fast-moving, creative, and progressive work environment. Ridgeline emphasizes a culture where employees act like owners, choose growth over comfort, communicate with transparency, assume positive intent, bias toward action, and bring solutions. The company fosters a resilient, collaborative, and high-performing culture, celebrating wins and learning from setbacks. Candidates must be work authorized in the United States without the need for employer sponsorship.

Requirements

10+ years of experience in software or data engineering with 5+ years in an engineering management role leading product or platform teams
Strong expertise in data modeling, data warehousing, and scalable reporting systems, including experience with distributed query engines such as Trino, Presto, or Spark SQL
Experience leading teams responsible for data platforms, analytics systems, or reporting infrastructure used broadly across an organization
Proven ability to deliver complex, high-impact initiatives in fast-paced environments while balancing customer-critical deliverables with long-term platform investments
Deep understanding of distributed systems, data pipelines, and performance optimization across compute- and IO-intensive workloads
Hands-on technical depth in at least one of the following areas: modern JVM languages such as Kotlin or Java, Python, or SQL-based analytics, along with familiarity with cloud data storage technologies
Strong communication skills with the ability to influence and align technical and non-technical stakeholders across functions
Demonstrated ability to hire, mentor, and retain high-performing engineering talent while supporting the growth of senior engineers and emerging leaders
Willingness to learn and apply cutting-edge technologies, including AI tooling, within a business-critical domain
Strong problem-solving skills with a bias toward ownership, action, and continuous improvement

Nice To Haves

Experience in fintech, investment management, or financial data systems, particularly in client or regulatory reporting
Familiarity with modern data stack technologies such as Snowflake, Databricks, or dbt
Experience building or operating reporting systems with strict correctness, auditability, and reliability requirements
Experience scaling teams and systems during periods of rapid organizational growth

Responsibilities

Lead and grow a high-performing engineering team responsible for the core reporting and data curation platform that underpins customer reports, dashboards, and internal analytics while establishing clear operating rhythms that keep the team aligned and effective
Define and execute the technical vision and roadmap for scalable operational reporting and high-quality data modeling, including evolving next-generation distributed query and reporting capabilities
Partner cross-functionally with product, design, and engineering teams to turn complex financial data into reliable, high-performance reports and data products while owning service health and operational excellence
Build and improve data pipelines, models, and tooling that ensure data is accurate, well-structured, and broadly reportable across systems
Establish and uphold best practices for data quality, governance, and consistency across reporting layers and shared data abstractions
Hire, coach, and develop engineers through clear feedback, growth planning, and mentorship while fostering a culture of accountability, learning, and psychological safety
Champion AI-native engineering workflows and modern development practices to improve delivery speed, system reliability, and developer experience