Data Management

17 hours ago

Pune, Maharashtra, India Vrinda Global Full time ₹ 12,00,000 - ₹ 36,00,000 per year

Qualifications:

  • Bachelor's or Master's degree in Data Science, Information Technology, Business Administration, or a related field.
  • Proven experience in winning and delivering accounts within the data management domain or similar industries.
  • 20+ years of experience in data-related project delivery, including 10+ years in a leadership or delivery management role.
  • Strong knowledge of data engineering, data warehousing, data lakes, cloud platforms (e.g., AWS, Azure, GCP), and data governance frameworks.
  • Experience with Agile, Scrum, and/or hybrid project management methodologies.
  • Excellent leadership, communication, and stakeholder engagement skills.
  • Proven ability to lead cross-functional teams in a matrixed organization structure.

Ability to oversee the technical aspects of data value chain

o Understand data ecosystem – data strategy aligning with business

goals, data governance, quality, medallion architecture, and so on

o Oversee the design and implementation of modern data platforms

o Manage proposals and projects involving cloud service providers

(e.g., AWS, Azure, Google Cloud)

o Ensure the efficient and secure integration of cloud services with

existing data systems

o Optimize cloud-based data storage and processing solutions for

performance and cost-effectiveness

-Experience with the key elements of data lifecycle while designing and

recommending data solutions to clients:

o Scalable Data Architectures

o Integration of various data sources, ensuring that data is collected,

processed, and made available in a structured manner.

o Data Security and Compliance: Ensure that data systems comply

with relevant security and privacy regulations, including GDPR,

HIPAA, etc., and are secure from threats.

o Data Pipeline Management: Design, development, and

optimization of data pipelines to ensure high-quality, efficient data

flow & storage.

o Performance Optimization: Continuously monitor and optimize

data workflows, addressing bottlenecks and improving speed.

o Ensure Data Quality: Robust data validation and quality checks to

ensure the accuracy, consistency, and completeness of the data

o Data Governance: Establish data governance frameworks,

policies, and processes to ensure proper data stewardship,

security, privacy, and compliance (e.g., GDPR, HIPAA).

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