IQVIA
Life Sciences
DataEngineer(DataOperations)
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“Data Engineer (Data Operations) at IQVIA. Skills: Data Engineering, Data Operations, Snowflake, SQL, Python. Build curated data layers. Maintain curated data layers”
Industry & Context.
Troubleshooting; Root cause analysis; Problem-solving
What They're Looking For.
Must Have
7+ years of experience in data engineering, 7+ years of experience in data operations, 7+ years of experience in backend data development, Hands-on experience with Snowflake, Hands-on experience with SQL, Hands-on experience with Python, Proven experience building production-grade data pipelines, Proven experience supporting production-grade data pipelines, Proven experience building production-grade data workflows, Proven experience supporting production-grade data workflows, Experience with ETL/ELT tools, Experience with orchestration frameworks, Experience with pipeline management tools, Experience troubleshooting data issues, Experience troubleshooting pipeline failures, Experience troubleshooting data inconsistencies, Root cause analysis skills, Solid understanding of data modeling, Solid understanding of data warehousing concepts, Solid understanding of data lifecycle management, Familiarity with CI/CD, Familiarity with version control (Git), Familiarity with production deployment practices, Problem-solving skills, Ability to work independently, Excellent communication skills
Nice to Have
Experience with R, Experience working with healthcare data, Experience working with pharmaceutical data, Experience working with life sciences data
What You'll Do.
Build curated data layers
Maintain curated data layers
Build curated data sources
Maintain curated data sources
Develop SQL transformations
Optimize SQL transformations
Develop Python transformations
Optimize Python transformations
Troubleshoot data quality issues
Identify root causes of data issues
Coordinate fixes for data issues
Validate incoming datasets
Ensure dataset accuracy
Ensure dataset completeness
Ensure dataset consistency
Design analytics-ready data models
Manage analytics-ready data models
Design analytics-ready tables
Manage analytics-ready tables
Serve as contact for reporting data layers
Ensure data sources meet usability needs
Ensure data sources meet performance needs
Monitor data pipelines for failures
Monitor data pipelines for anomalies
Monitor data pipelines for performance issues
Monitor datasets for failures
Monitor datasets for anomalies
Monitor datasets for performance issues
Resolve pipeline failures
Resolve dataset anomalies
Resolve performance issues
Support production operations
Partner with engineering teams
Escalate ingestion issues
Resolve ingestion issues
Improve data validation processes
Improve data monitoring processes
Improve operational efficiency
How You'll Work.
Team & Collaboration
BI and analytics teams; Upstream engineering teams; Technical stakeholders; Non-technical stakeholders
Communication Scope
Explain data issues; Explain data solutions
Process & Methodology
Incident management, Backlog prioritization, SLA adherence
Full Job Description
**The Data Engineer (Data Operations**) is responsible for building, maintaining, and optimizing downstream data pipelines and analytics-ready datasets that power business intelligence and reporting. This role focuses on data troubleshooting, transformation, and delivery, ensuring that upstream data is validated and converted into reliable, curated data sources for BI and analytics teams. The individual will work hands-on with Snowflake, SQL, and Python to develop data models, resolve data issues, and support production data operations, enabling the BI team to efficiently consume high-quality data through their visualization tools. Experience with R is a plus, as we transition from R into new technologies. _**Ess ential Functions** _ * Build and maintain curated data layers and data sources * Develop and optimize SQL- and Python-based transformations in Snowflake * Troubleshoot data quality issues originating from upstream pipelines, including identifying root causes and coordinating fixes where needed * Validate incoming datasets and ensure accuracy, completeness, and consistency before they are surfaced to downstream users * Design and manage analytics-ready data models and tables to support reporting and dashboards * Serve as a key point of contact for reporting data layers ensuring data sources meet usability and performance needs * Monitor data pipelines and datasets for failures, anomalies, and performance issues, and proactively resolve them * Support production operations, including incident management, backlog prioritization, and SLA adherence * Partner with upstream engineering teams to escalate and resolve ingestion-related issues, while not owning ingestion directly * Improve processes for data validation, monitoring, and operational efficiency _**Qualifications** _ * Bachelor’s degree * 7+ years of experience in data engineering, data operations, or backend data development * Strong hands-on experience with: * Snowflake (data warehouse design, optimizat
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