Abbott's Lingo platform needs to evolve its multi-region enterprise data platform to ensure systems are scalable, secure, compliant, and able to power innovation across product, marketing, and R&D, enabling global scale, accelerating product innovation, and delivering real-time, reliable analytics to customers and partners.
Requirements
- 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.
- Azure and Databricks experience; exposure to service meshes/Kubernetes and cloud-native data platform patterns; cost optimization at scale.
Responsibilities
- 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).
Other
- 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.
- 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.
