Senior Manager, Data Engineering

Openkrill; United States; Hybrid; USD 180,000 - 240,000; Full time

Benefits offered by this job

Flexible paid time off

Medical, dental, and vision insurance

401(k) retirement plan with matching

Paid family leave

Annual wellness subsidy

Job summary

Openkrill is seeking a Senior Manager, Data Engineering in the United States to lead the development of a scalable data platform supporting analytics, reporting, and future AI capabilities. The role combines strategic leadership with hands-on technical engagement in a modern cloud environment.

You will mentor a team, define architecture standards, and collaborate across product, finance, risk, and analytics to deliver reliable, well-governed data solutions while optimizing cost and performance.

Qualifications

• Experience leading data engineering teams and setting technical direction.
• Deep hands-on knowledge of Snowflake, dbt, SQL, and ETL/ELT architectures.
• Strong understanding of data modeling, Medallion architecture, and modern data warehouses.
• Experience with data quality, governance, observability, and security practices.
• Cloud-based data platforms experience with cost and performance optimization.
• Ability to work at architectural and implementation levels with customers and stakeholders.
• Proven leadership and mentoring abilities to grow engineers into owners.

Responsibilities

• Lead the Data Engineering team, set technical direction and long-term roadmap.
• Own architecture and performance of Snowflake, PostgreSQL, and data pipelines.
• Drive transition to Medallion architecture and define data standards.
• Manage modern data stack and related technologies (Snowflake, Fivetran, Meltano, AWS).
• Collaborate with cross-functional teams to design scalable data solutions.
• Establish data quality, testing, governance, and documentation practices.
• Improve self-service data access and reduce engineering dependency.
• Mentor Data Engineers and foster ownership and collaboration.
• Balance business impact with long-term platform sustainability.
• Communicate trade-offs and investment needs to leadership.

Skills

Leadership

Mentoring

Team management

Strategic planning

Communication

Hands-on problem solving

Architectural thinking

Tools

Snowflake

dbt

Fivetran

Meltano

PostgreSQL

AWS

ETL/ELT architectures

Python

Ruby

Job description

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Senior Manager, Data Engineering based in the United States.

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.

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..*[Wait!]*