Data - Lead

• The

  Lead Data Architect

  is a strategic and technical leadership role responsible for architecting, scaling, and evolving enterprise-grade data platforms that enable advanced analytics, AI/ML, and data-driven decision-making. Reporting to the

  Senior Director of Data Platforms

  , this role will lead the design and governance of modern data architectures, drive innovation in AI orchestration, and ensure the delivery of secure, compliant, and high-performing data solutions.
• This position combines hands-on engineering expertise with architectural vision and cross-functional leadership. The Lead Data Architect will guide engineering teams, influence platform strategy, and establish best practices across the organizations data ecosystem.

Key Responsibilities

• Architect Scalable Data Platforms

  : Design and oversee implementation of robust data lakehouse, data mesh, and real-time streaming architectures to support enterprise analytics and AI/ML initiatives.

• Lead Engineering Teams

  : Provide technical leadership and mentorship to data engineers, fostering a culture of excellence, innovation, and continuous improvement.

• AI/ML Enablement

  : Collaborate with Data Science and ML Engineering teams to operationalize models, implement AI orchestration frameworks (e.g., MLflow, Airflow), and ensure scalable deployment pipelines.

• Platform Strategy & Governance

  : Define and enforce architectural standards, data governance policies, and compliance frameworks (HIPAA, SOC 2, GDPR, etc.) across the data platform.

• Performance & Reliability Optimization

  : Drive observability, automation, and performance tuning across data pipelines and infrastructure to ensure reliability at scale.

• Cross-Functional Collaboration

  : Partner with product, analytics, compliance, and infrastructure teams to align data architecture with business goals and regulatory requirements.

• Innovation & Thought Leadership

  : Stay ahead of industry trends, evaluate emerging technologies, and contribute to strategic decisions on platform evolution, including generative AI integration and event-driven systems.

Basic Qualifications

• Bachelors or Masters degree in Computer Science, Engineering, Data Science, or related field.
• 10+ years of experience in data engineering and architecture, with a proven track record of leading large-scale data initiatives.
• Deep expertise in Python, Spark, and Databricks (Delta Lake, MLflow).
• Strong experience with orchestration tools (Airflow, Prefect, Dagster) and cloud platforms (AWS, Azure, GCP).
• Demonstrated success in architecting and deploying AI/ML pipelines in production.
• Strong understanding of data governance, privacy, and compliance in regulated industries (healthcare, life sciences).

Preferred Qualifications

• Experience with event-driven architectures (Kafka, Kinesis) and real-time data processing.
• Advanced knowledge of data modeling, lakehouse/lake/warehouse design, and performance optimization.
• Familiarity with generative AI platforms and use cases.
• Contributions to open-source projects or thought leadership in data engineering/architecture.
• Experience with Agile methodologies, CI/CD, and DevOps practices.
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