Senior Data Engineer

Medical Guardian•Philadelphia, PA

13d•Hybrid

About The Position

Medical Guardian is a fast-growing digital health and safety company on a mission to help people live a life without limits. With 13 consecutive years on the Inc. 5000 list of Fastest Growing Companies, we’re redefining what it means to age confidently and independently. We support over 625,000 members nationwide with life-saving emergency response systems and remote patient monitoring solutions. Trusted by families, healthcare providers, and care managers, our work is powered by a culture of innovation, compassion, and purpose. This role is focused on building real time streaming data solutions that enable data-driven decisions on live devices and telemetry data. While the Senior Data Engineer will support batch data processes, the primary focus is on real-time streaming use cases, data services for APIs and microservices, and pipelines that feed both operational applications and ML/AI model development in a medical device IoT environment. As a Senior Data Engineer, you will play a key role in driving the design and architecture of data pipelines that consume real-time streaming data from connected medical devices. You will collaborate cross-functionally with business analysts, software engineers, machine learning engineers, and business users to implement technical approaches and infrastructure that support data consumption across the organization. This is a great opportunity for someone who is passionate about data engineering, thrives in environments where you can take ownership and recommend solutions, and is a self-starter who is open to learning new tools as needed.

Requirements

Bachelor's degree in Computer Science, Mathematics, Engineering, or a related technical field and 6+ years of professional experience in data engineering, analytics, or warehousing OR Master's degree in a related technical field and 3+ years of professional experience in data engineering, analytics, or warehousing
5+ years of experience designing, building, and operating big data and real-time streaming pipelines across cloud and on-premises environments
5+ years applying DevOps and CI/CD practices to data and analytics workloads
Production experience building data services, APIs, or microservices for downstream data consumption.
Strong, hands-on experience designing and building production-grade data pipelines on Databricks
Demonstrated experience with Spark optimization and tuning in Databricks, including performance analysis, partitioning strategies, caching, shuffle optimization, and cost-aware pipeline design
Strong experience with Azure cloud services for data engineering and streaming workloads
Experience building data services, APIs, or microservices for data consumption.
Data quality, validation, and privacy-aware handling for regulated or sensitive data.
Candidates must be authorized to work in the United States without current or future need for visa sponsorship.
Candidates must be able to commute to our Philadelphia, PA office on Tuesdays and Wednesdays.

Nice To Haves

Azure Event Hubs and Azure Stream Analytics for IoT and telemetry ingestion and stream processing
Additional Azure data and integration services supporting batch and streaming architectures
Collaboration with ML teams: feature pipelines, training data, or streaming data for model development

Responsibilities

Design and build batch and streaming data pipelines on Azure and Databricks, with primary focus on real-time IoT and telemetry use cases within a Medallion architecture.
Develop ETL/ELT workflows to ingest, transform, and validate large volumes of structured and unstructured data.
Build and maintain data services, APIs, and microservices for application, analytics, and ML/AI teams.
Implement real-time streaming solutions using Azure Event Hubs, Azure Stream Analytics, and related Azure integration patterns, with cost-effective throughput, partitioning, and downstream delivery to Databricks.
Optimize production Databricks pipelines using PySpark, Spark SQL, and Delta Lake, including Spark tuning for performance, reliability, and cost.
Troubleshoot and resolve complex pipeline issues across Databricks, Azure, and on-premises systems, including root cause analysis and corrective action.
Partner with data analysts, software engineers, ML engineers, and business stakeholders to translate requirements into technical designs and delivery priorities.
Apply data quality, validation, and privacy-first practices, and deliver reliable pipelines through software engineering standards, documentation, testing, and CI/CD.