Senior Engineering Manager Data Architecture

Lingo Data Architecture Leader

Lingo is building the future of personal health insights through always-on biosensor data, real-time analytics, and personalized recommendations. Our platform ingests millions of readings daily, delivering clinically relevant insights to consumers and partners across the globe. As we expand across multiple regions, data architecture is mission-critical, ensuring our systems are scalable, secure, compliant, and able to power innovation across product, marketing, and R&D.

We are seeking a proven leader to own and evolve Lingo's multi-region enterprise data platform. This role will drive the enterprise data architecture that powers Lingo's growth and trusted insights. Your decisions will enable global scale, accelerate product innovation, and deliver real-time, reliable analytics to customers and partners. As we expand, this role offers a path to Director-level leadership and broader influence across our global data footprint.

What You'll Work On

• Own the enterprise data reference architecture and roadmap across operational, analytical, and ML domains; ensure interoperability, consistent metadata, and scalable data contracts.
• Architect and evolve event-driven pipelines (Kafka ? Databricks/Spark ? analytics stores) and CDC pathways for real-time and batch workloads with HA/DR patterns.
• Design data architectures optimized for AI/ML workloads; enable seamless integration of models into batch and streaming pipelines for real-time insights.
• Leverage Azure and Databricks to support AI-driven decision-making and predictive analytics; partner with data science to operationalize models using event-driven architectures and modern orchestration tools.
• Champion best practices for governance, quality, lineage, and retention to power trustworthy AI applications; implement automated, auditable controls that ensure HIPAA/GDPR compliance-by-design.
• Define and track data SLOs (freshness, accuracy, availability, latency, cost); lead capacity planning, observability, and incident playbooks for data services.
• Design storage strategies for performance and cost (partitioning, sharding, tiered storage) and tune databases for high-volume time-series and transactional workloads; plan for scale (e.g., 100M+ daily biosensor readings across regions).
• Foster a data-driven culture: promote data literacy, launch training programs, and evangelize best practices across engineering, product, and business teams.
• Collaborate cross-functionally with Backend, Cloud/Platform, Security, Product, and Compliance to remove obstacles in a regulated, medical device software environment.
• Lead and mentor the data engineering team; establish standards, conduct reviews, and drive continuous improvement in delivery and reliability.

Required Qualifications

• 10+ years in data architecture for high-volume, distributed systems, including multi-region production platforms.
• Deep expertise in event streaming and large-scale ETL/ELT with Kafka and Databricks or Spark; strong SQL and data modeling (3NF, dimensional, time-series).
• Hands-on experience optimizing databases, indexing, storage tiers, and CDC patterns.
• Proven track record implementing automated governance, lineage, and compliance frameworks (HIPAA, GDPR) in enterprise environments; ability to support trustworthy AI through rigorous data controls.
• Experience enabling AI/ML workloads in production (feature pipelines, model data interfaces, real-time inference).
• Demonstrated success in regulated environments; understands medical device software realities and how to stay compliant while shipping quickly.
• Strong cross-functional leadership and stakeholder alignment; effective at road mapping, trade-offs, and communicating impact to business outcomes.
• Team leadership: mentoring engineers, establishing standards, and improving team velocity and quality.

Preferred Qualifications

• Experience in digital health, Medtech, or other regulated SaaS environments; familiarity with BI tooling, analytics APIs, and self-service strategies.
• Azure and Databricks experience; exposure to service meshes/Kubernetes and cloud-native data platform patterns; cost optimization at scale.
• Experience with ML feature stores, model-serving data interfaces, or metrics platforms. Knowledge of data mesh principles, enterprise metadata management, and federated governance.
• Track record fostering data literacy and running enablement programs across teams.

The base pay for this position is $146,700.00 $293,300.00. In specific locations, the pay range may vary from the range posted.