Data Engineering Manager I

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.

Core Responsibilities & Management Expectations

People Management & Mentorship

• Direct Supervision: Conduct effective 1:1s, set clear goals, deliver regular performance feedback, and manage career growth for direct reports.
• Engineering Culture: Foster a psychologically safe team environment built on trust, transparency, continuous improvement, and a growth mindset.
• Team Upskilling: Actively mentor engineers in data engineering best practices, design patterns, testing strategies, and operational discipline.

Team Leadership & Process Stewardship

• Delivery & Execution: Act as a team steward, driving execution, managing team velocity, breaking down bottlenecks, and maintaining operational sustainability.
• Agile & Work Management: Facilitate planning, standups, and prioritization; partner with data analysts and business leaders to translate business needs into well-defined, incremental stories.
• Process Critique & Improvement: Regularly evaluate team processes to refine norms and eliminate friction.

Cross-Functional Collaboration & Stakeholder Alignment

• Stakeholder Enablement: Serve as the primary technical contact for analytics and business leaders to ensure data enablement across departments.
• Project Communication: Maintain radical transparency on team roadmaps, risks, delays, and progress.
• Business & Strategy Alignment: Translate complex technical concepts into business terms for cross-functional partners and leadership.

Technical Governance & Architecture Leadership

• AI-Ready Platform Architecture: Direct the design and scale of our Snowflake-based data platform, ingestion architecture, and AI interface layers.
• Enterprise Integrations: Oversee seamless data ingestion from core systems (e.g., Salesforce, HubSpot, NetSuite, Zendesk, Jira, Confluence) into a centralized, queryable source of truth.
• Data Governance & Observability: Establish company-wide standards for data ownership, access management, lineage visibility, testing/validation, and automated pipeline monitoring.

Performance Objectives

Within 30 Days

• Team & Management Foundation: Establish recurring 1:1s with direct reports, assess team dynamics and individual skill sets, and align on individual goals and development paths.
• Stakeholder Mapping: Build working relationships with key cross-functional stakeholders to map out current data dependencies, pain points, and reporting workflows.
• Full Data Ecosystem Audit: 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

• Operational Execution: Refine team operating rhythms (planning, standups, retro) and prioritization processes to improve delivery transparency and reduce operational friction for analytics requests.
• Standards & Quick Wins: Define foundational engineering standards (code review, dbt testing/modeling guidelines, monitoring/alerting) and implement 1–2 quick wins to improve immediate pipeline reliability.
• Architectural Options Drafting: 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

• Architecture Recommendation: 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.
• Self-Service & Governance: Establish company-wide standards for data ownership, lineage tracking, and access management to promote secure self-service analytics.
• Team Maturity: Demonstrate measurable improvements in delivery consistency, pipeline reliability, analyst trust, and structured team mentorship.

Within 6 Months

• Phase 1 Platform Implementation: Successfully execute the initial phase of the approved architecture recommendation, delivering core, production-ready pipelines and well-documented datasets.
• High-Performing Culture: Build an engineering culture centered on technical excellence, operational discipline, psychological safety, and clear ownership.
• Strategic Value: Position the data engineering function as a proactive, strategic enabler for company-wide AI and data initiatives.

Success in the Role Looks Like

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