Principal Data Engineer (1044) - Data Enablement - DataSF

City and County of San Francisco•San Francisco, CA

13d•Hybrid

About The Position

As DataSF grows to support every city department and their diverse needs, we’re formalizing the data practice that sits around the platform—clear service offerings, consistent onboarding, intuitive self‑service tools, and reliable data quality standards. This role is the principal engineer leading that effort. You’ll own the department‑facing experience: how teams learn to work with DataSF, what they can expect from our services, and how projects are defined and delivered. You’ll combine strong platform and data engineering depth with the ability to translate departmental needs into actionable, scalable solutions. Starting as an individual contributor partnering closely with two analytics engineers, the role may evolve to manage a small team as our practice grows.

Requirements

An associate degree in computer science, computer engineering, information systems, or a closely related field from an accredited college or university OR its equivalent in terms of total course credits/units [i.e., at least sixty (60) semester or ninety (90) quarter credits/units with a minimum of twenty (20) semester or thirty (30) quarter credits/units in one of the fields above or a closely-related field].
Five (5) years of experience analyzing, installing, configuring, enhancing, and/or maintaining the components of a system or platform.
One year of additional experience as described above may be substituted for the required degree.
Completion of the 1010 Information Systems Trainee Program may be substituted for the required degree.

Nice To Haves

Strong hands-on data platform or data engineering background: building and operating pipelines, data modeling, transformation frameworks, and platform governance.
Analytics engineering depth in dbt, semantic modeling, or comparable.
Cloud data warehouse experience (Snowflake preferred) and advanced SQL.
Data governance, privacy, or security awareness, including handling of sensitive data.
Demonstrated strength working directly with non-technical stakeholders to understand their needs, translating them into technical work, and driving adoption.
Experience getting people to actually use a platform, not just building one: onboarding, enablement, training, or change management.
Comfort setting expectations and holding boundaries with demanding stakeholders while maintaining the relationship.
Experience mentoring engineers or analysts and raising the standard of a team's work.
Genuine interest in public-sector impact and a clear-eyed understanding of what working in government is like.
Commitment to equity, transparency, and ethical data use in service of San Francisco's residents.

Responsibilities

Own the full department engagement lifecycle—from initial conversation through scoping, onboarding, delivery, and ongoing support.
Define and publish the DataSF service catalog, clearly outlining what constitutes standard queue work, what requires a scoped project, and what triggers an interagency agreement, using measurable criteria.
Partner directly with departments to understand and model their data domains, translating those needs into actionable requirements the platform can serve.
Shape the analytics engineering practice, including dbt models, department pipelines, data quality approaches, and reusable standards the team relies on.
Build self‑service pathways that allow departments to discover data, assess its trustworthiness, and request access without needing to know whom to contact.
Own data quality standards and trust signals—freshness, completeness, lineage—so users understand what assumptions they can make about each dataset.
Integrate governance and data‑readiness requirements into the onboarding process rather than treating them as afterthoughts.
Set and maintain clear scope expectations with departments and perform related duties as assigned.