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Senior Project Manager - Analytics

Philips · Bengaluru

Senior 🇬🇧 English
PMP Data Warehousing Big Data Data Engineering Data Science Complex Event Processing Real-time Analytics Data Visualization SQL Python MATLAB SAS

Job description

About the role

We are looking for an experienced Senior Project Manager to lead end‑to‑end delivery of data and analytics initiatives. The role combines project management, technical data expertise, and stakeholder engagement, primarily supporting Supply Chain business teams.

Key responsibilities

  • Lead full lifecycle project management for analytics and data initiatives.
  • Engage senior stakeholders, provide updates, resolve issues, and align priorities.
  • Evaluate existing reporting environments and define data requirements.
  • Drive data strategy and architecture from design through execution.
  • Lead technical delivery, ensuring quality, scalability, and timeliness.
  • Design and document complex data and analytics solutions.
  • Troubleshoot production issues and support end‑users with reporting queries.
  • Promote best practices in instrumentation, experimentation, and analytics delivery.
  • Design and implement database structures, models, and architectures.
  • Oversee data migration from legacy systems to modern platforms.
  • Monitor system performance, conduct testing, and drive continuous improvement.
  • Provide training and support to internal teams on data systems and tools.

Required profile

  • 8–11 years of progressive experience in analytics, data engineering, or related domains.
  • Proven consulting or delivery leadership experience, including opportunity shaping, solution design, and execution.
  • PMP or equivalent project management certification (mandatory).
  • Strong communication, executive presentation, and problem‑solving skills.
  • Highly driven, adaptable, and able to thrive in fast‑paced environments.

Required skills

  • Data Warehousing & Big Data Technologies
  • Data Engineering & Data Science
  • Complex Event Processing & Real‑time Analytics
  • Data Visualization tools and frameworks
  • End‑to‑end data architecture design
  • Data ingestion, pipeline development, storage, and processing
  • Machine learning algorithms and analytics tools
  • Python, R, MATLAB, or SAS
  • SQL and relational databases
  • Cloud‑based data platforms and architectures

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Published 4 months ago

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Philips

Bengaluru