Data Engineering Manager – Augmedix

Data Engineering Manager – Augmedix

Requirements

• 2-4 years of experience in data science, analytics, consulting, investment banking, or operations at a fast-paced SaaS tech company
• Deep experience in data analysis (both SQL and Excel strongly preferred) – basic open-source SQL assessment will be part of the interview process
• Build data pipelines, analytics infrastructure, and reporting layers to drive operational visibility
• Lead and coordinate remote teams contributing to data/analytics workstreams
• Serve as the primary point of contact for internal analytics needs and tool development
• Collaborate cross-functionally with Product, Engineering, and Ops to translate business needs into technical solutions
• Proficiency in documenting and developing best practices to enable incremental improvement and optimization of operational processes
• Demonstrated drive, intellectual curiosity, attention to detail, and a proven record of success
• Comfort in chaos: You’re comfortable working autonomously to solve problems with ambiguity, can expertly prioritize, and have a knack for identifying internal and external blockers
• Customer obsessed: You’re going to dig until you hit bedrock to solve a problem for the customer, because we need to ensure our clients have the cash they need to pay the doctors and nurses that work on the front lines of healthcare
• Interface with clients (approx. 20% of the time) to explain data insights and support issue resolution
• Proficient in Retool, G-Suite is a plus

Responsibilities

• Lead, mentor, and manage a team of data engineers and operations analysts to deliver high-quality data and dashboard solutions. Upon starting the role, the Senior Manager will be responsible for uniting two separate teams: Data Engineering and Operations Analysts
• Define team goals, set priorities, and manage workloads to meet deadlines and business objectives
• Provide technical guidance and coaching to team members, fostering a culture of innovation and continuous improvement
• Conduct regular performance reviews, provide constructive feedback, and support career development
• Design and implement scalable and secure data pipelines to support data acquisition, processing, and storage
• Develop and maintain data models, warehouses, and lakes to support business intelligence and analytics
• Ensure data infrastructure meets performance, scalability, and security requirements. This includes quickly coming up to speed on the current complex database architecture and evaluating a potential re-architecture to improve performance
• Identify and implement best practices for data governance, data quality, and data security
• Establish data monitoring and alerting systems to ensure data integrity and minimize downtime
• Build and optimize ETL/ELT processes to transform and load data efficiently
• Automate data workflows to improve performance and reduce manual effort
• Implement data cleansing, transformation, and enrichment processes to ensure data consistency and accuracy
• Introduce machine learning models and algorithms where applicable to enhance data processing and insights generation
• Collaborate with software engineering, product management, and operations teams to integrate data systems with core products
• Provide data insights and reports to support business decisions and strategic initiatives
• Act as a liaison between technical and non-technical teams to translate business requirements into data solutions

Preferred Qualifications

• Experience in the Ambient Documentation (AI Scribe) space is a plus