Yochana
Contract Role: Azure Data Engineer at Pleasanton, California (Onsite) - W2 Only
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an Azure Data Engineer in Pleasanton, California, offering a long-term W2 contract. Key skills include Azure Databricks, Azure Data Factory, SQL, and data pipeline development. Familiarity with Delta Lake and CI/CD practices is preferred.
🌎 - Country
United States
💱 - Currency
$ USD
-
💰 - Day rate
Unknown
-
🗓️ - Date
July 14, 2026
🕒 - Duration
More than 6 months
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🏝️ - Location
On-site
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📄 - Contract
W2 Contractor
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🔒 - Security
Unknown
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📍 - Location detailed
Pleasanton, CA
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🧠 - Skills detailed
#Triggers #GIT #Spark (Apache Spark) #Spark SQL #Cloud #Azure Databricks #Synapse #Data Lake #Azure DevOps #Monitoring #Data Pipeline #SQL (Structured Query Language) #Delta Lake #Data Engineering #Datasets #ADLS (Azure Data Lake Storage) #Azure Repos #Databricks #Agile #Data Quality #Azure #ADF (Azure Data Factory) #Azure Data Factory #Scala #Data Integration #PySpark #Version Control #Data Modeling #Azure ADLS (Azure Data Lake Storage) #Storage #Vault #Deployment #"ETL (Extract #Transform #Load)" #DevOps
Role description
Azure Data Engineer
Pleasanton, California (Onsite - all 5 days in a week )
Long Term Contract
Employment Type – W2
Mandatory Skills: Azure Databricks (ADB), Azure Data Factory (ADF), Azure Analysis Service (AAS), SQL
Job Summary
• We are seeking a skilled Azure Data Engineer with strong hands-on expertise in Azure Databricks (ADB), Azure Data Factory (ADF), and SQL. The ideal candidate will design and build scalable data pipelines and transformations on the Azure platform, with solid experience in Spark (PySpark), the Databricks Lakehouse, and ETL/ELT orchestration. Exposure to Delta Lake, data modeling, and CI/CD for data workloads is a plus.
Key Responsibilities
• Design, develop, and maintain data pipelines using Azure Databricks (PySpark/Spark SQL) and Delta Lake.
• Build and orchestrate ETL/ELT workflows in Azure Data Factory, including linked services, datasets, and triggers.
• Write and optimize complex SQL for data transformation, aggregation, and performance tuning.
• Collaborate with cross-functional teams to deliver reliable, high-quality data solutions.
• Implement data quality checks, monitoring, and follow engineering best practices.
• Work in an Agile environment and contribute to sprint planning and retrospectives.
Skill Requirements
• Databricks: Strong proficiency in Azure Databricks, PySpark, Spark SQL, and notebook development.
• Data Integration: Hands-on experience with Azure Data Factory (pipelines, mapping data flows, triggers).
• Database: Expert-level SQL (mandatory) with T-SQL/Spark SQL; query optimization and stored procedures.
• Storage & Format: Experience with Delta Lake, Parquet, and Azure Data Lake Storage (ADLS Gen2).
• Data Modeling: Understanding of dimensional modeling and data warehousing concepts.
• Version Control: Git/Azure Repos or similar tools.
• CI/CD: Familiarity with Azure DevOps pipelines for data deployments.
• Cloud: Working knowledge of the broader Azure ecosystem (Key Vault, Synapse is a plus).
Azure Data Engineer
Pleasanton, California (Onsite - all 5 days in a week )
Long Term Contract
Employment Type – W2
Mandatory Skills: Azure Databricks (ADB), Azure Data Factory (ADF), Azure Analysis Service (AAS), SQL
Job Summary
• We are seeking a skilled Azure Data Engineer with strong hands-on expertise in Azure Databricks (ADB), Azure Data Factory (ADF), and SQL. The ideal candidate will design and build scalable data pipelines and transformations on the Azure platform, with solid experience in Spark (PySpark), the Databricks Lakehouse, and ETL/ELT orchestration. Exposure to Delta Lake, data modeling, and CI/CD for data workloads is a plus.
Key Responsibilities
• Design, develop, and maintain data pipelines using Azure Databricks (PySpark/Spark SQL) and Delta Lake.
• Build and orchestrate ETL/ELT workflows in Azure Data Factory, including linked services, datasets, and triggers.
• Write and optimize complex SQL for data transformation, aggregation, and performance tuning.
• Collaborate with cross-functional teams to deliver reliable, high-quality data solutions.
• Implement data quality checks, monitoring, and follow engineering best practices.
• Work in an Agile environment and contribute to sprint planning and retrospectives.
Skill Requirements
• Databricks: Strong proficiency in Azure Databricks, PySpark, Spark SQL, and notebook development.
• Data Integration: Hands-on experience with Azure Data Factory (pipelines, mapping data flows, triggers).
• Database: Expert-level SQL (mandatory) with T-SQL/Spark SQL; query optimization and stored procedures.
• Storage & Format: Experience with Delta Lake, Parquet, and Azure Data Lake Storage (ADLS Gen2).
• Data Modeling: Understanding of dimensional modeling and data warehousing concepts.
• Version Control: Git/Azure Repos or similar tools.
• CI/CD: Familiarity with Azure DevOps pipelines for data deployments.
• Cloud: Working knowledge of the broader Azure ecosystem (Key Vault, Synapse is a plus).
