[Hiring] Data Engineering Manager @OpenSesame

Role Description

The Data Engineering Manager will guide and grow a lean, focused data engineering team. The team’s mission is to build and scale an AI-ready data foundation that enables reliable analytics, operational reporting, governance, enterprise system integrations, and future AI initiatives across the company. This role partners closely with analysts, business leaders, engineering teams, product management, and data consumers company-wide to improve the accessibility, trust, and usability of enterprise data assets.

As a Data Engineering Manager I, you will combine direct people management, team stewardship, operational execution, and technical decision-making. You will own the Data Governance & Accessibility program while serving as the managerial and architectural lead responsible for designing scalable data systems, improving pipeline reliability, and enabling analysts through robust, specialized data solutions.

Success in this role requires balancing high-impact people management (1:1s, performance management, career development) with strategic planning, cross-functional stakeholder management, agile process facilitation, and technical leadership.

Core Responsibilities & Management Expectations

• People Management & Mentorship
  • Conduct effective 1:1s, set clear goals, deliver regular performance feedback, and manage career growth for direct reports.
  • Foster a psychologically safe team environment built on trust, transparency, continuous improvement, and a growth mindset.
  • Actively mentor engineers in data engineering best practices, design patterns, testing strategies, and operational discipline.
• Team Leadership & Process Stewardship
  • Act as a team steward, driving execution, managing team velocity, breaking down bottlenecks, and maintaining operational sustainability.
  • Facilitate planning, standups, and prioritization; partner with data analysts and business leaders to translate business needs into well-defined, incremental stories.
  • Regularly evaluate team processes to refine norms and eliminate friction.
• Cross-Functional Collaboration & Stakeholder Alignment
  • Serve as the primary technical contact for analytics and business leaders to ensure data enablement across departments.
  • Maintain radical transparency on team roadmaps, risks, delays, and progress.
  • Translate complex technical concepts into business terms for cross-functional partners and leadership.
• Technical Governance & Architecture Leadership
  • Direct the design and scale of our Snowflake-based data platform, ingestion architecture, and AI interface layers.
  • Oversee seamless data ingestion from core systems (e.g., Salesforce, HubSpot, NetSuite, Zendesk, Jira, Confluence) into a centralized, queryable source of truth.
  • Establish company-wide standards for data ownership, access management, lineage visibility, testing/validation, and automated pipeline monitoring.

Performance Objectives

• Within 30 Days
  • Establish recurring 1:1s with direct reports, assess team dynamics and individual skill sets, and align on individual goals and development paths.
  • Build working relationships with key cross-functional stakeholders to map out current data dependencies, pain points, and reporting workflows.
  • Conduct a comprehensive audit of the end-to-end data stack (Snowflake, Fivetran, dbt, Looker, etc.) to evaluate cloud spend, pipeline reliability, data transformations, data quality gaps, and constraints for future AI initiatives.
• Within 60 Days
  • Refine team operating rhythms (planning, standups, retro) and prioritization processes to improve delivery transparency and reduce operational friction for analytics requests.
  • Define foundational engineering standards (code review, dbt testing/modeling guidelines, monitoring/alerting) and implement 1–2 quick wins to improve immediate pipeline reliability.
  • Draft initial proposals for a modernized, cost-effective data architecture across ingestion, transformation, and storage, evaluating trade-offs between current tooling, usage-based pricing, and AI integration requirements.
• Within 90 Days
  • Deliver a formal, well-documented recommendation for a modernized data architecture across the entire stack that optimizes tool and infrastructure costs, improves processing efficiency, and provides a scalable foundation for AI solutions.
  • Establish company-wide standards for data ownership, lineage tracking, and access management to promote secure self-service analytics.
  • Demonstrate measurable improvements in delivery consistency, pipeline reliability, analyst trust, and structured team mentorship.
• Within 6 Months
  • Successfully execute the initial phase of the approved architecture recommendation, delivering core, production-ready pipelines and well-documented datasets.
  • Build an engineering culture centered on technical excellence, operational discipline, psychological safety, and clear ownership.
  • Position the data engineering function as a proactive, strategic enabler for company-wide AI and data initiatives.

Success in the Role Looks Like

• Direct reports feel supported, clear on expectations, and are actively growing in their technical and professional skills.
• Analysts across the organization reliably access trusted, well-documented datasets with minimal operational friction.
• Data pipelines run with high reliability, clear SLAs, and proactive alerting.
• The enterprise data platform cleanly supports AI/LLM experimentation and plain-language analytics without requiring major re-architecting.
• Stakeholders across OpenSesame view the data engineering team as a collaborative, highly effective driver of business value.

Location

This position can be based anywhere in the US. We operate as a remote-first company and invest in all-company in-person meetings several times a year.

Compensation

The salary for this role ranges between $180,000 - $208,000 per year, depending on experience.

Benefits

• Comprehensive medical, dental, and vision coverage.
• Flexible PTO.
• 401(k) matching.
• Incentive stock options.
• Volunteer time off.
• Employee Resource Groups.
• A generous professional development program with dedicated annual time off for learning and growth.