Senior Manager, Data Engineering

Accountabilities

• Lead the Data Engineering team, establish technical direction, and develop a long-term roadmap aligned with business priorities and future platform needs.

• Own the architecture, reliability, performance, scalability, and ongoing evolution of Snowflake, PostgreSQL, and data pipelines.

• Lead the transition to a Medallion architecture and define standards for data ingestion, transformation, modeling, testing, documentation, and consumption.

• Manage and optimize the modern data stack, including Snowflake, dbt, Fivetran, Meltano, PostgreSQL, AWS, and related technologies.

• Partner with application engineering teams on transactional database design, schema changes, query performance, scalability, and reliability.

• Collaborate with Engineering, Analytics, Product, Finance, Risk, Marketing, and other stakeholders to develop reusable data solutions that address business needs.

• Establish and maintain engineering best practices for data quality, automated testing, observability, data lineage, governance, security, documentation, and incident response.

• Improve self-service data access, enabling teams to discover, understand, and use trusted data without unnecessary engineering dependencies.

• Monitor and optimize platform performance, infrastructure scalability, and operational costs.

• Lead, mentor, and develop Data Engineers, fostering technical ownership, accountability, collaboration, and continuous improvement.

• Guide architecture and design decisions, review technical approaches, challenge assumptions, and contribute hands-on expertise to complex engineering problems when needed.

• Prioritize work according to business impact, technical risk, operational reliability, and long-term value.

• Communicate technical decisions, trade-offs, risks, and investment needs clearly to technical teams, business stakeholders, and leadership.

• Evaluate opportunities to support advanced analytics, automation, and AI-powered applications through a scalable, well-governed data platform.

Requirements:

• At least 6 years of experience in Data Engineering, Data Platform Engineering, Database Engineering, or a related technical field.

• At least 2 years of experience leading or managing Data Engineers or other technical teams.

• Strong hands-on experience with Snowflake, dbt, SQL, and modern ETL/ELT architectures.

• Solid knowledge of PostgreSQL or comparable relational databases, including schema design, indexing, query optimization, and reliability.

• Experience with data ingestion and integration tools such as Fivetran, Meltano, or equivalent technologies.

• Understanding of dimensional modeling, Medallion architecture, and modern data warehouse design patterns.

• Strong knowledge of data quality, automated testing, observability, lineage, governance, security, and documentation practices.

• Experience operating cloud-based data platforms, with an understanding of scalability, performance optimization, and cost management.

• Ability to work at both the architectural and implementation levels, combining strategic planning with hands-on technical problem-solving.

• Demonstrated leadership, mentoring, and team-development skills, with the ability to establish clear standards while empowering engineers to own their work.

• Excellent communication and collaboration skills, including the ability to translate technical concepts into clear recommendations for business and executive stakeholders.

• Ability to manage competing priorities and build solutions that balance immediate business requirements with long-term platform sustainability.

• Experience with Python or Ruby for automation, tooling, or data processing is a plus.

• Experience in financial services or another regulated industry is desirable.

• Familiarity with analytics and AI applications, data catalogs, data contracts, or master data management is advantageous.

• Experience leading a major data warehouse or platform modernization initiative is a plus.

• Familiarity with applying AI and automation to engineering productivity, monitoring, documentation, or data quality is beneficial.

Benefits:

• Flexible paid time off.

• Medical, dental, and vision insurance with generous plan options.

• 401(k) retirement plan with employer matching contributions.

• Paid family leave.

• Annual wellness subsidy.

• Work-from-home subsidy.

• Employee referral program.

• Remote-first work culture, with access to a conveniently located downtown office.

• Annual salary range of$135,000–$175,000.