When joining PerkinElmer, you select an experienced and trusted leader in scientific solutions, with the support of a global service network and distribution centers, providing the right solution, at the right time, to meet critical customer needs. With over an 80+ year legacy of advancing science and a mission of innovating for a healthier world, our dedicated team collaborates closely with commercial, government, academic and healthcare customers to deliver our broad portfolio of analytical solutions, and OneSource services.

Job Title

Director - Data Analytics

Location(s)

Mumbai

Preferred Characteristics

Experience within Life Sciences, Diagnostics, Healthcare, or related scientific industries.
Direct experience in designing and implementing solutions specifically for data platform modernization.
Experience managing globally distributed teams.
Experience implementing DataOps and CI/CD practices within a data engineering context.
Ability to balance operational excellence with innovation, driving both cost efficiency and business value creation.
Ability to drive outcomes in a highly complex matrix environment which is often amorphous.
Lead and develop a high-performing global team of functional experts, technical leads, and application managers.
Foster a culture of accountability, collaboration, innovation, and customer focus.
Continuously upskill the team to stay aligned with evolving technology trends and business needs.

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.