MM International, LLC
Data Engineer / Databricks Developer (Onsite Interview)
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Engineer / Databricks Developer in New York, requiring over 6 months of full-time commitment. Key skills include Azure Databricks, Apache Spark, and SQL, with mandatory experience in Investment Banking. Onsite work is required 3 days/week.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
April 14, 2026
🕒 - Duration
More than 6 months
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🏝️ - Location
On-site
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
New York, United States
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🧠 - Skills detailed
#Data Lineage #"ETL (Extract #Transform #Load)" #Azure Databricks #Security #Data Quality #Datasets #Azure #Data Architecture #Data Engineering #PySpark #Cloud #Spark (Apache Spark) #ADF (Azure Data Factory) #Data Access #Azure Data Factory #SQL (Structured Query Language) #MDM (Master Data Management) #Data Processing #Data Pipeline #Compliance #Databricks #Data Governance #Data Management #Data Integrity #Apache Spark #Scala
Role description
Job Title: Data Engineer / Databricks Developer
Location: New York (Onsite – 3 days/week)
Employment Type: Full-time
Note: Only local candidates will be considered. In-person interview is mandatory.
Job Overview
We are seeking an experienced Data Engineer / Databricks Developer with a strong background in the Investment Banking domain to join our team in New York. The ideal candidate will be responsible for designing, developing, and optimizing scalable data solutions using Azure-based technologies, with a focus on Databricks and modern data architecture.
Key Responsibilities
• Design, develop, and implement scalable data pipelines using Azure Databricks and Azure Data Factory.
• Build and maintain robust ETL/ELT workflows using Apache Spark and PySpark DataFrames.
• Develop high-performance data transformation processes using Spark and SQL.
• Optimize data pipelines for efficient ingestion, processing, and transformation of large datasets.
• Implement and maintain data governance frameworks, including data access, security, compliance, and lifecycle management.
• Ensure data quality, consistency, and lineage across enterprise data platforms.
• Design and support modern Lakehouse architecture and distributed data processing systems.
• Apply Master Data Management (MDM) principles to maintain data integrity and standardization.
• Collaborate with cross-functional teams to deliver scalable, reliable, enterprise-grade data solutions.
Required Skills & Qualifications
• Strong hands-on experience with Azure Databricks
• Proficiency in Apache Spark / PySpark
• Experience with Azure Data Factory
• Advanced knowledge of SQL
• Expertise in building data pipelines and ETL/ELT processes
• Experience with data governance, data lineage, and data quality frameworks
• Strong understanding of Lakehouse architecture
• Experience working in Investment Banking or Financial Services domain (Mandatory)
Preferred Qualifications
• Experience with large-scale distributed data systems
• Familiarity with cloud-based data platforms and enterprise data architecture
• Strong problem-solving and performance optimization skills
Job Title: Data Engineer / Databricks Developer
Location: New York (Onsite – 3 days/week)
Employment Type: Full-time
Note: Only local candidates will be considered. In-person interview is mandatory.
Job Overview
We are seeking an experienced Data Engineer / Databricks Developer with a strong background in the Investment Banking domain to join our team in New York. The ideal candidate will be responsible for designing, developing, and optimizing scalable data solutions using Azure-based technologies, with a focus on Databricks and modern data architecture.
Key Responsibilities
• Design, develop, and implement scalable data pipelines using Azure Databricks and Azure Data Factory.
• Build and maintain robust ETL/ELT workflows using Apache Spark and PySpark DataFrames.
• Develop high-performance data transformation processes using Spark and SQL.
• Optimize data pipelines for efficient ingestion, processing, and transformation of large datasets.
• Implement and maintain data governance frameworks, including data access, security, compliance, and lifecycle management.
• Ensure data quality, consistency, and lineage across enterprise data platforms.
• Design and support modern Lakehouse architecture and distributed data processing systems.
• Apply Master Data Management (MDM) principles to maintain data integrity and standardization.
• Collaborate with cross-functional teams to deliver scalable, reliable, enterprise-grade data solutions.
Required Skills & Qualifications
• Strong hands-on experience with Azure Databricks
• Proficiency in Apache Spark / PySpark
• Experience with Azure Data Factory
• Advanced knowledge of SQL
• Expertise in building data pipelines and ETL/ELT processes
• Experience with data governance, data lineage, and data quality frameworks
• Strong understanding of Lakehouse architecture
• Experience working in Investment Banking or Financial Services domain (Mandatory)
Preferred Qualifications
• Experience with large-scale distributed data systems
• Familiarity with cloud-based data platforms and enterprise data architecture
• Strong problem-solving and performance optimization skills
