Director - Data Analytics

PerkinElmer


Date: 3 weeks ago
City: Thāne, Maharashtra
Contract type: Full time
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.

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