IQVIA

Life Sciences

DataEngineer(DataOperations)

$69–173k Rosemont, Illinois, United States FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“Data Engineer (Data Operations) at IQVIA. Skills: Data Engineering, Data Operations, Snowflake, SQL, Python. Build curated data layers. Maintain curated data layers”

Industry & Context.

Life Sciences
Problems you'll solve

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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