Associate Director, Data Engineer
S&P Global (company site, opens in a new tab)8+ yrsGurugramHybridDirect career pageEst. 35–55 LPA
Posted 19h ago
Reach people at S&P Global
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at S&P Global you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Design, build, and maintain scalable ETL/ELT data pipelines and lakehouse datasets used across the organization.
- Own the quality, integrity, and documentation of enterprise-wide data products and Power BI reports to support decision-making.
- Lead technical standards, mentor team members, and collaborate with stakeholders to deliver data-driven solutions using Databricks, Microsoft Fabric, Azure, and Power BI.
Minimum Requirements
- 8+ years of experience in data engineering, business intelligence, or related fields with senior technical contributor experience.
- Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or related discipline.
- Proficiency with ETL/ELT pipelines, Python, advanced SQL, Power BI (DAX, Power Query), Databricks, Microsoft Fabric, and Azure cloud services.
- Work Experience Required: 8+ years in relevant fields. Notice period: Not explicitly mentioned in the JD.
Ideal Candidate Profile
- Experienced senior data engineer comfortable owning and improving complex data architectures and pipelines end-to-end in enterprise environments.
- Strong technical leadership skills with ability to mentor team members, establish best practices, and drive strategic initiatives for data product quality and efficiency.
- Proven ability to translate business data needs into scalable technical solutions and work collaboratively across product, scrum teams, and stakeholders.