Lead Data Engineer

Cognizant

Confirmed live today High trust

Quick summary

Work type: On-site
Location: Las Vegas, NV
Posted: 22 days ago
Freshness: Confirmed live today
Nearby: 47 roles within 25 mi

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Similar $170k

$116k $223k

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This listing doesn't post a salary. Most similar roles pay $126,800–$213,000.

Based on 240 similar postings.

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About Cognizant

Cognizant is a multinational IT services and consulting company offering digital transformation, technology, and business process services. It serves clients across financial services, healthcare, manufacturing, and other industries worldwide.

Cognizant currently has 330 open roles on FindRole.

Listed pay typically runs $100,000–$135,000 across 211 roles with salary data.

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At a glance

TL;DR · Lead Data Engineer

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The Lead Data Engineer will join the engineering team to design and develop high-performance data pipelines and scalable data solutions. This role involves leading end-to-end data engineering projects from initial design through production, including optimizing Spark workloads, clusters, and jobs for reliability and cost. The engineer will integrate data from multiple sources into analytics-ready datasets while establishing coding standards, reusable frameworks, and data quality monitoring. Key technical requirements include deep expertise in Azure Databricks, Spark, PySpark, Python, and SQL. Candidates should also possess experience with ETL/ELT pipelines, data modeling, and distributed data processing. Preferred skills include Delta Lake, ADLS, ADF, Unity Catalog, CI/CD, and Azure DevOps. The work focuses on solving real-world data challenges within hospitality, travel, booking, customer, or operational data domains.

Skills

Azure Databricks Spark PySpark Python SQL ETL ELT Delta Lake ADLS ADF Unity Catalog CI/CD Azure DevOps Data Modeling Distributed Data Processing

What you'll do

Design and build scalable data pipelines using Azure Databricks, Spark, and PySpark.
Lead end-to-end data engineering solutions from initial design through production.
Optimize Spark workloads, clusters, and jobs for performance, reliability, and cost.
Integrate data from multiple sources into trusted, analytics-ready datasets.
Build data quality, monitoring, and validation into every pipeline.
Establish coding standards, reusable frameworks, and engineering best practices.
Mentor engineers through design reviews, code reviews, and technical guidance.
Explore and implement new Databricks and Spark capabilities into production.

What we're looking for

Must have 13+ years of total experience in the data warehouse space.
Must have 5+ years of hands-on experience with Azure Databricks.
Deep expertise in Spark, PySpark, Python, and SQL is required.
Experience building large-scale ETL/ELT data pipelines is required.
Strong understanding of data modeling and distributed data processing is required.
Experience with performance tuning and Databricks cluster optimization is required.
Knowledge of Delta Lake, ADLS, ADF, Unity Catalog, CI/CD, and Azure DevOps is a plus (preferred).
Experience working with hospitality, travel, booking, customer, or operational data is a plus (preferred).

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