Engineering Manager - Data Engineering

Upside•Austin, TX

1d•Hybrid

About The Position

We are looking for an Engineering Manager to lead our Data Engineering team, the owners of Upside’s core data platform. This team provides the ingestion, modeling, orchestration, and tooling capabilities that power analytics and data-driven decisions across the company. You’ll combine technical acumen with people-first leadership to scale our platform, foster engineering growth, and drive data accessibility and trust across internal teams. This role will also help close a strategic gap in our technical organization by guiding the evolution of MLOps practices to support the development, deployment, and monitoring of machine learning models used in personalization and optimization features.

Requirements

3+ years of experience managing data, analytics, or ML engineering teams
At least 3+ years of hands-on experience as an individual contributor building data products, pipelines, or ML systems.
Proficient with modern data platforms and tooling, including Snowflake, dbt, and Dagster, and are fluent in core concepts like modeling, orchestration, and data transformation.
Hands-on experience or deep familiarity with MLOps practices, such as model versioning, deployment pipelines, monitoring, and reproducibility.
Thrive in environments where you’re asked to scale teams, elevate systems, and bring clarity to ambiguity.
Comfortable designing and reviewing solutions in AWS environments, and make thoughtful tradeoffs to balance performance, cost, and maintainability.
Eager to integrate generative AI tools into development workflows to accelerate delivery and improve the developer experience.
Communicate clearly across technical and non-technical audiences, and can advocate effectively for platform investments that support long-term business value.

Responsibilities

Co-Create and drive the vision and roadmap for Upside’s data platform, shaping the strategy for data across Upside in alignment with company goals.
Ensure our data stack, which includes Snowflake, dbt, and Dagster, enables scalable, self-service, and trustworthy workflows for reporting, analytics, and experimentation.
Drive strategic data platform initiatives that improve reproducibility, reliability, and analytics enablement—such as modernizing legacy infrastructure, implementing tools like Snowflake Semantic Views and Cortex, and transforming 3rd-party data into trusted, production-ready assets.
Define and track key platform health metrics, including pipeline reliability, SLA adherence, cost-efficiency, and model deployment readiness.
Partner cross-functionally with Product, Engineering, Data Science, and GTM stakeholders to ensure the data platform supports current and emerging business needs.
Represent Analytics Engineering in company-wide planning forums, technical councils, and cross-functional working groups.
Create a safe, collaborative team environment.
Operate with a first-team mindset.
Raise the bar by identifying and influencing improvements in quality, performance, speed of execution, and engineering standards.
Grow talent by coaching engineers, recognizing achievements, and helping them develop skills.
Set clarity and empower autonomy by articulating expectations and direction.