Principal Data Engineer

About Medical Guardian

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 are 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.

Key Responsibilities

• Hands-On Data Engineering and Platform Development
• Design, build, optimize, and operate production-grade batch and streaming data pipelines on Azure and Databricks, with a primary focus on real-time IoT and telemetry use cases within a Medallion architecture.
• Develop ETL/ELT workflows to ingest, transform, validate, and serve large volumes of structured, semi-structured, unstructured, and streaming data.
• Build and maintain reliable data products, data services, APIs, and microservices that support operational applications, analytics, software engineering, and ML/AI teams.
• Use Python, PySpark, Spark SQL, SQL, Delta Lake, Databricks Workflows, CI/CD, and related tools to build maintainable, testable, and observable data systems.
• Troubleshoot complex production pipeline issues across Databricks, Azure, streaming systems, APIs, and source systems, including root cause analysis, corrective action, and prevention planning.
• Move quickly from rough business need to prototype, pilot, and production-ready data capability while maintaining appropriate engineering discipline.
• Real-Time Streaming, IoT Telemetry, and Operational Data Services
• Lead the design and delivery of real-time streaming ingestion and processing patterns for connected medical device telemetry, event data, and operational data feeds.
• Implement streaming solutions using Azure Event Hubs, Azure Stream Analytics, Databricks, Delta Lake, and related Azure integration patterns.
• Create consumption patterns that support APIs, microservices, operational applications, near-real-time decisioning, analytics, and ML/AI use cases.
• Define reliability, latency, quality, observability, and supportability expectations for production streaming systems.
• Databricks, Lakehouse, and Data Platform Architecture
• Set direction for Databricks-based data engineering patterns, including Medallion architecture, Delta Lake, Spark optimization, data modeling, data quality, and reusable pipeline design.
• Optimize production Databricks pipelines using PySpark, Spark SQL, Delta Lake, partitioning strategies, caching, shuffle optimization, cluster/job configuration, and cost-aware design.
• Establish practical standards for pipeline structure, code organization, testing, deployment, monitoring, documentation, and ownership.
• Partner with data platform, security, infrastructure, and engineering teams to ensure the data platform is scalable, secure, reliable, and aligned with enterprise architecture.
• Make pragmatic architecture tradeoffs between speed, durability, cost, governance, performance, and downstream business impact.
• Stakeholder Shaping and Cross-Functional Partnership
• Work directly with business, product, analytics, ML/AI, operations, software engineering, and leadership stakeholders to clarify what data is needed, why it matters, how it will be used, and what success looks like.
• Translate ambiguous business needs into concrete data requirements, data product definitions, architecture options, delivery priorities, and implementation plans.
• Ask practical questions early: who will use the data, what decision or workflow does it support, what latency and quality are required, what happens if the data is wrong or late, and how will we know the capability is creating value?
• Help the organization avoid becoming a data ticket factory by shaping solutions, not just executing requests.
• Communicate architecture decisions, tradeoffs, risks, dependencies, and delivery options clearly to technical and non-technical stakeholders.
• Team Management and Principal-Level Technical Leadership
• Manage, mentor, and develop data engineers, providing clear expectations, technical guidance, prioritization support, feedback, and accountability.
• Provide technical leadership through hands-on example, strong engineering judgment, clear recommendations, and pragmatic decision-making.
• Lead design reviews, code reviews, production readiness reviews, incident reviews, and architecture discussions across data engineering initiatives.
• Establish and improve engineering standards for data quality, testing, CI/CD, observability, documentation, runbooks, cost management, privacy, and security.
• Proactively identify platform risks, data gaps, unclear ownership, operational weaknesses, and opportunities to improve reliability, scalability, and delivery speed.
• Influence without relying only on formal authority by building trust, framing tradeoffs, and helping cross-functional teams get to decisions.
• ML/AI, Analytics, and GenAI Enablement
• Partner with ML engineers, data scientists, analytics engineers, and analysts to deliver reliable data pipelines, feature pipelines, training datasets, scoring inputs, and feedback loops.
• Support the data foundation for predictive models, risk scores, operational decisioning, GenAI workflows, RAG, document intelligence, summarization, and AI-enabled automation.
• Help define data contracts, model-ready datasets, feature definitions, lineage, and monitoring expectations for ML/AI and analytics use cases.
• Ensure downstream consumers understand the meaning, freshness, quality, limitations, and appropriate use of the data products they depend on.
• Governance, Data Quality, Security, and Production Operations
• Apply privacy-first, security-aware, and governance-aligned practices for regulated, sensitive, and operationally critical data.
• Design and implement data quality checks, validation rules, anomaly detection, schema expectations, alerting, and operational monitoring.
• Ensure production pipelines and services are supportable, observable, documented, recoverable, and aligned with business continuity needs.
• Drive incident response and continuous improvement for data platform and pipeline issues, including root cause analysis and preventative remediation.
• Balance innovation with reliability, compliance, privacy, cost discipline, and operational usefulness.

Required Qualifications

• 10+ years of professional experience in data engineering, software engineering, data platform engineering, distributed systems, analytics engineering, or related technical fields.
• 7+ years of hands-on experience designing, building, optimizing, and operating production data pipelines, data platforms, or data services.
• 5+ years of hands-on experience with modern cloud data platforms, including Databricks, Spark, Delta Lake, SQL, Python/PySpark, and production pipeline orchestration.
• 3+ years of experience leading, managing, mentoring, or providing technical direction to data engineers or related technical teams.
• Strong experience with Azure cloud services for data engineering, streaming, integration, storage, security, and production operations.
• Experience designing and operating real-time streaming, event-driven, or near-real-time data pipelines in production or business-critical environments.
• Experience applying DevOps, CI/CD, testing, version control, code review, documentation, and automation practices to data engineering workloads.
• Experience building data services, APIs, microservices, or reusable consumption patterns for downstream applications, analytics, ML/AI, or operational workflows.
• Strong understanding of data quality, observability, monitoring, lineage, reliability, cost optimization, privacy, and production support for data systems.
• Experience translating ambiguous business needs into technical designs, architecture recommendations, delivery plans, and measurable outcomes.
• Ability to explain data architecture, pipeline behavior, tradeoffs, assumptions, risks, and limitations to both technical and non-technical stakeholders.
• Strong ownership mindset and ability to drive work forward independently in a fast-moving, evolving environment.

Preferred Qualifications

• 12+ years of relevant professional experience in data engineering, software engineering, data platforms, distributed systems, analytics engineering, commercial software, or production data products.
• Experience working as a principal, staff, lead, manager, or architect-level data engineering leader in a production environment.
• Experience managing direct reports, setting team priorities, developing engineers, and improving team execution and accountability.
• Experience working in commercial software, SaaS, digital products, healthtech, fintech, IoT, consumer technology, or other product-driven environments.
• Experience in startup, scale-up, innovation, new product development, or rapid-build environments where the candidate had to operate with ambiguity and drive work forward independently.
• Experience with Azure Event Hubs, Azure Stream Analytics, Azure Service Bus, Azure Data Factory, Azure Functions, ADLS, Azure Cosmos.