Senior Manager, Data Engineering

United StatesRemoteFull-timesenior

EngineeringData Engineering

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Requirements Summary

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

Technical Tools

EngineeringData Engineering

As Senior Manager, Data Engineering, you will lead the development and evolution of a scalable data platform that supports analytics, reporting, operational decision-making, and future AI-powered capabilities. You’ll set the technical direction for modern data architecture while building and mentoring a high-performing engineering team. Working across engineering, product, finance, risk, marketing, and analytics, you’ll transform business needs into reliable, reusable data solutions. You’ll lead data warehouse modernization, strengthen governance and quality standards, and optimize platform performance and costs. This role combines strategic leadership with hands-on technical involvement in a modern cloud-based data environment. The position offers remote flexibility and the opportunity to shape the long-term data capabilities of a growing organization.

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

~2 min read

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.