Specialist EDM Data Quality

Job Description Summary

Internal Job Title: Specialist DDIT APD EDM Data Quality
#LI-Hybrid
Location: Hyderabad, India
Relocation Support: Yes

Great data drives great decisions, and great decisions can change lives. As a Specialist DDIT APD EDM Data Quality at Novartis, you will strengthen the quality, trust, and governance of enterprise data supporting critical business and healthcare outcomes. You will partner with data, governance, and technology leaders to shape and scale Data Quality Management capabilities across the organization, helping teams transform data into a strategic asset. If you are passionate about data excellence, governance, and measurable business impact, this is an opportunity to help reimagine medicine through the power of high-quality data.

 

Job Description

Key Responsibilities

·    Drive adoption of enterprise Data Quality Management frameworks, standards, methodologies, and best practices across data domains.

·    Understand the working of enterprise data quality tools and support their ongoing enhancement, and adoption.

·    Participate and partner with Enterprise Data Governance teams in governance forums and decision-making discussions.

·    Establish domain-level baselines through critical data element identification and use-case prioritization.

·    Understand the end to end DQ Lifecyle and support the business teams to implement, and maintain data quality rules, metrics, thresholds, and monitoring processes.

·    Analyze data quality trends and guide domains for their remediation activities that improve data accuracy and reliability.

·    Work with EDM team, IT team & business stakeholder to enhance the DQ dashboard with right KPIs to measure ands monitor the DQ progress for each business stakeholder

·    Establish business intelligence solutions like PowerBI dashboards for measuring and monitoring DQ outcomes

·    Champion enterprise data management capabilities and foster a culture of data excellence across Novartis.

Essential Requirements

·    Bachelor’s degree in computer science/ information technology, data science, data management, or a related discipline.

·    5+ years of experience in data management, specifically Data Quality management, including at least 2 years in pharmaceutical or healthcare environments.

·    Hands-on experience implementing data quality rules using Ataccama or comparable platforms

·    Strong knowledge of Data Quality Management processes like finalizing data for DQ execution, data profiling, building rules, remediation, monitoring, measurement, and audit practices.

·    Experience with data governance and master data management tools such as Ataccama, Collibra, Informatica, or EBX.

·    Strong analytical, statistical, stakeholder management, and communication skills within global matrix organizations.

Desirable Requirements

·    Experience supporting enterprise-wide data governance and audit programs.

·    Experience identifying, analyzing data anomalies and applying advanced analytical techniques to drive continuous improvement.

 

Commitment to Diversity and Inclusion:

Novartis is committed to building an outstanding, inclusive work environment and diverse teams'

representative of the patients and communities we serve.

 

Accessibility and accommodation

Novartis is committed to working with and providing reasonable accommodation to individuals with

disabilities. If, because of a medical condition or disability, you need a reasonable accommodation for

any part of the recruitment process, or in order to perform the essential functions of a position, please

send an e-mail to [email protected] and let us know the nature of your request and

your contact information. Please include the job requisition number in your message

 

Skills Desired

Biostatistics, Curious Mindset, Data Governance, Data Literacy, Data Quality, Data Science, Data Visualization, Graph Algorithms, Learning Agility, Machine Learning (ML), Machine Learning Algorithms, Python (Programming Language), Statistical Analysis, Time Series Analysis