Not Applicable
Specialism
Data, Analytics & AI
Management Level
Senior Associate
& Summary
.
In data analysis at PwC, you will focus on utilising advanced analytical techniques to extract insights from large datasets and drive datadriven decisionmaking. You will leverage skills in data manipulation, visualisation, and statistical modelling to support clients in solving complex business problems.
Why PWC
At PwC , you will be part of a vibrant community of solvers that leads with trust and creates distinctive outcomes for our clients and communities. This purposeled and valuesdriven work, powered by technology in an environment that drives innovation, will enable you to make a tangible impact in the real world. We reward your contributions, support your wellbeing, and offer inclusive benefits, flexibility programmes and mentorship that will help you thrive in work and life. Together, we grow, learn, care, collaborate, and create a future of infinite experiences for each other. Learn more about us .
At PwC , we believe in providing equal employment opportunities, without any discrimination on the grounds of gender, ethnic background, age, disability, marital status, sexual orientation, pregnancy, gender identity or expression, religion or other beliefs, perceived differences and status protected by law. We strive to create an environment where each one of our people can bring their true selves and contribute to their personal growth and the firm s growth. To enable this, we have zero tolerance for any discrimination and harassment based on the above considerations.
& Summary The role is part of the Data Observability Specialist, being part of Enabling Data Platforms, with the aim to ensure data systems remain reliable, transparent, and aligned with organizational goals, enabling other teams to confidently use data for decisionmaking and innovation. Chapter ensures that data systems, pipelines and models are monitored, traceable and optimized for reliability and performance.
s
Supporting the implementation of best practices and guidelines for data observability across the organization supporting the alignment of observability aligns with data governance, quality, and compliance frameworks acting as the bridge between data observability technology and business needs collaborating with data stewards, governance teams, and business units to ensure data is reliable, transparent, and accessible training and educating teams on data observability concepts and tools collaborating to define performance indicators (KPIs) for data health, including completeness, accuracy, timeliness and resources consumption supporting clear visibility into data lineage, helping teams understand the origin, transformation, and usage of data across the ecosystem partnering with Data Governance and Engineering teams to improve lineage tracking and documentation supporting business teams in understanding the impact of data anomalies and outages supporting the response efforts from a functional perspective, ensuring that businesscritical data remains available and trustworthy using lineage insights to trace root causes of data issues and prevent recurrence monitoring that observability practices align with internal policies and regulatory requirements (e.g., GDPR) providing functional insights on data lineage, security, and compliance implications monitoring that lineage tracking supports auditability and accountability for data governance.
Mandatory skill sets
Must have knowledge, skills and experiences 5+ years of experience in Data Management, Data Quality and possibly Data Engineering hands on experience working with data observability, data lineage, or data quality frameworks in largescale organizations hands on experience with data observability tools (e.g., Monte Carlo, Soda, Great Expectations, Collibra, Alation, Data Bricks)
Preferred skill sets Good to have knowledge, skills and experiences
a Bachelor s or Master s degree in Data Management, Computer Science, Information Systems, Business Analytics, or a related field strong understanding of data lineage, data quality metrics, and data monitoring methodologies experience with data platforms and cloud environments (AWS, Azure, or GCP) knowledge of SQL and ability to interpret technical data flows an understanding of ETL/ELT processes and how data moves across the organization strong problemsolving skills with the ability to analyze data issues and recommend functional improvements excellent communication and stakeholder management skills to translate technical insights into business value excellent written and verbal English familiarity with data observability tools good knowledge of data governance frameworks and standards. Good to have familiarity with data governance frameworks (e.g., DAMA DMBOK, DCAM) and regulatory compliance (e.g., GDPR)
Years of experience required
Experience and Qualifications 5+ Years
Education qualification
o BE, B.Tech, ME, M,Tech, MBA, MCA (60% above)
Education
Degrees/Field of Study required Bachelor of Engineering, Master of Business Administration, Master of Engineering
Degrees/Field of Study preferred
Required Skills
Data Standardization
Accepting Feedback, Accepting Feedback, Active Listening, Algorithm Development, Alteryx (Automation Platform), Analytical Thinking, Analytic Research, Big Data, Business Data Analytics, Communication, Complex Data Analysis, Conducting Research, Creativity, Customer Analysis, Customer Needs Analysis, Dashboard Creation, Data Analysis, Data Analysis Software, Data Collection, DataDriven Insights, Data Integration, Data Integrity, Data Mining, Data Modeling, Data Pipeline {+ 38 more}
No
