Director - Data Analytics

Key Responsibilities:

Platform Stabilization & Operational Excellence:

• Accountable for stable, reliable, and secure operations across all Datawarehouse applications, ensuring adherence to defined SLAs and KPIs.
• Assess the current data platform architecture, identify bottlenecks, and implement solutions to ensure high availability, reliability, performance, and scalability.
• Establish robust monitoring, alerting, and incident management processes for all data pipelines and infrastructure.
• Drive initiatives to improve data quality, consistency, and trustworthiness across the platform.
• Oversee the operational health and day-to-day management of existing data systems during the transition period.
• Manage relationships with strategic vendors across the enterprise applications landscape, ensuring strong performance, innovation contributions, and commercial value.
  

Platform Modernization & Architecture:

• Define and execute a strategic roadmap for modernizing PerkinElmer's data platform, leveraging cloud-native technologies (AWS, Azure, or GCP) and modern data stack components (e.g., data lakes/lakehouses, Data Fabric/Mesh architectures, streaming platforms like Kafka/Kinesis, orchestration tools like Airflow, ELT/ETL tools, containerization).
• Lead the design and implementation of a scalable, resilient, and cost-effective data architecture that meets current and future business needs. (DaaS)
• Champion and implement DataOps principles, including CI/CD, automated testing, and infrastructure-as-code, to improve development velocity and reliability.
• Stay abreast of emerging technologies and industry trends, evaluating and recommending new tools and techniques to enhance the platform.
  

Leadership & Strategy:

• Build, mentor, and lead a world-class data engineering team, fostering a culture of innovation, collaboration, and continuous improvement.
• Develop and manage the data engineering budget, resources, and vendor relationships.
• Define the overall data engineering vision, strategy, and multi-year roadmap in alignment with PerkinElmer's business objectives.
• Effectively communicate strategy, progress, and challenges to executive leadership and key stakeholders across the organization.
• Drive cross-functional collaboration with IT, Security, Enterprise Apps, R&D, and Business Units.
  

Data Monetization Enablement:

• Partner closely with business leaders, enterprise app teams, and other business teams to understand data needs and identify opportunities for data monetization.
• Architect data solutions, APIs, and data products that enable the creation of new revenue streams or significant internal efficiencies derived from data assets.
• Ensure robust data governance, security, and privacy controls are embedded within the platform design and data products, adhering to relevant regulations (e.g., GDPR, HIPAA where applicable).
• Build the foundational data infrastructure required to support advanced analytics, machine learning, and AI initiatives.
• Basic Qualifications
  

Required Qualifications & Experience

• Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or a related quantitative field.
• 15+ years of experience in data engineering, data architecture and/or data warehousing.
• 5+ years of experience in a leadership role, managing data engineering teams and driving large-scale data initiatives.
• Proven track record of successfully leading the stabilization, modernization, and scaling of complex data platforms.
• Deep expertise in modern data architecture patterns (Data Lakes, Data Warehouses, Lakehouses, Lambda/Kappa architectures).
• Extensive hands-on experience with cloud data platforms (AWS, Azure, or GCP – specify preferred if applicable) and their associated data services (e.g., S3/ADLS/GCS, Redshift/Synapse/BigQuery, EMR/Dataproc/Databricks, Kinesis/Kafka/Event Hubs, Glue/Data Factory/Dataflow).
• Strong experience with big data technologies (e.g., Spark, Hadoop ecosystem) and data processing frameworks.
• Proficiency with data pipeline orchestration tools (e.g., Airflow, Prefect, Dagster).
• Solid understanding of SQL and NoSQL databases, data modeling techniques, and ETL/ELT development.
• Experience with programming languages commonly used in data engineering (e.g., Python, Scala, Java).
• Excellent understanding of data governance, data security, and data privacy principles and best practices.
• Exceptional leadership, communication, stakeholder management, and strategic thinking skills.
• Demonstrated ability to translate business requirements into technical solutions.