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Tech Stack
Tools & technologiesAirflowAmazon RedshiftBigQueryCloudPythonSQL
About the role
Key responsibilities & impact- Act as the overall technical leader for data engineering, owning architecture direction, setting standards, and making design decisions
- Lead high-impact, cross-functional initiatives end to end, from scoping and sequencing through delivery
- Partner with the manager on team and project management processes, including sprint planning and team processes
- Drive organization-wide technical alignment through staff engineering leadership forums and shared initiatives
- Serve as thought leader on AI and agentic workflows in data engineering
- Architect and scale batch and streaming ingestion frameworks for internal systems, third-party APIs, and streaming events
- Own and evolve machine learning deployment architecture, including infrastructure and patterns for production model serving and monitoring
- Define and enforce data schemas and best practices for data modeling, schema evolution, and data lifecycle management
- Own data governance strategy, including data quality, metadata management, lineage, security, privacy, and compliance
- Design and optimize system-wide data architecture for analytics, agentic, and operational use cases
- Partner with product, engineering, analytics, compliance, Operations, Finance, Risk, and other business functions
- Mentor engineers through code quality, architecture reviews, technical design discussions, and effective AI tooling
- Drive adoption of modern orchestration, lineage-tracking, and cataloging tools
Requirements
What you’ll need- Bachelor’s or Master’s degree in Computer Science (or related technical field)
- 8+ years of data engineering experience, with at least 3+ years in senior or staff-level roles
- Track record as a tech lead, owning technical direction for a team or major platform area and delivering multi-quarter initiatives with several engineers and cross-functional partners
- Hands-on experience with AI-assisted and agentic engineering
- Proven expertise in data ingestion frameworks (batch and streaming)
- Deep experience with cloud-native data platforms (Snowflake, BigQuery, Databricks, or Redshift)
- Experience building or owning machine learning deployment infrastructure, including feature pipelines, model serving, and production monitoring
- Strong command of SQL and Python
- Hands-on knowledge of data governance frameworks, including quality checks, lineage, cataloging, security, and compliance
- Strong understanding of data warehouse methodologies, dimensional and normalized modeling, and distributed system design
- Experience with orchestration tools such as Airflow, Dagster, or Prefect
- Experience with metadata/catalog platforms such as dbt, DataHub, Amundsen, or similar
- Ability to set long-term technical vision while driving execution on near-term deliverables
- Excellent communication and leadership skills, with a track record of mentoring engineers and influencing cross-functional stakeholders and senior leaders
Benefits
Comp & perks- Commuter benefits
- Lunches and snacks
- Collaborative, high-growth environment
- Equity in a high-growth startup
- Multiple health benefit options
- Responsible Time Off
- 401(k) matching
- Opportunities and support for major career growth
- Annual Company offsite event (Mudfest!)