Data Engineer with Financial Services, Databricks and MDM Experience

This position, based in Jersey City, NJ on a hybrid schedule, seeks a hands-on Data Engineer with Financial Services experience to architect and maintain scalable data pipelines, master data management integrations, and cloud-native platforms such as Databricks and Snowflake to support operational, analytical, and regulatory data needs.

Responsibilities

• Design, develop, and support scalable data pipelines and enterprise data integration solutions.
• Build and maintain batch and real-time data ingestion, transformation, and processing frameworks.
• Develop cloud-native data engineering solutions for enterprise data lake, data warehouse, and lakehouse environments.
• Implement ETL and ELT processes for structured, semi-structured, and unstructured data sources.
• Support Master Data Management initiatives across security, account, client, and reference data domains.
• Collaborate with data architects, business analysts, governance teams, and application groups to advance enterprise data initiatives.
• Implement data quality validation, monitoring, metadata management, and data lineage processes.
• Assist cloud migration and modernization efforts for legacy and enterprise data platforms.
• Optimize data processing, storage, and pipeline performance for scalability and operational efficiency.
• Ensure compliance with security, governance, and regulatory standards within financial services environments.
• Support reporting, analytics, and downstream consumption platforms through reliable data delivery.

Requirements

• Strong hands-on experience in data engineering and enterprise-scale data integration.
• Proven ability to develop scalable ETL/ELT pipelines and distributed data processing solutions.
• Experience with modern cloud-based data platforms and data ecosystems.
• Proficiency in SQL and programming or scripting languages such as Python, PySpark, or Snowpark.
• dbt expertise for data transformation and modeling, ELT in Snowflake/Databricks, modular SQL workflows, testing, documentation, and version control integration.
• Experience with cloud platforms (Azure, AWS, or GCP) and integration with Snowflake and Databricks.
• Solid understanding of data lake, data warehouse, and lakehouse architectures across platforms.
• Experience with orchestration and workflow tools such as Airflow, Databricks Workflows, or Snowflake Tasks.
• Experience supporting Master Data Management and enterprise data governance initiatives.
• Familiarity with metadata management, data lineage, data cataloging, and data quality processes.
• Experience integrating APIs and microservices, as well as batch file-based ingestion.
• Real-time streaming experience (e.g., Kafka, Spark Streaming).

Must Have Skills

• Financial services experience
• Data pipeline development
• Databricks and Delta Lake
• Master Data Management (MDM) with Golden Source integration
• Strong SQL and data engineering capabilities
• Oracle to cloud migration experience
• Data quality and reconciliation

Technologies

• Databricks, Delta Lake, Snowflake, Snowpark
• Python, PySpark, SQL, dbt
• Azure, AWS, GCP
• Airflow, Databricks Workflows, Snowflake Tasks
• Kafka, Spark Streaming

Benefits

• Medical, Prescription, Dental & Vision Benefits (for employees working 20+ hours per week)
• Health Savings Account (HSA) (for employees working 20+ hours per week)
• Life & Disability Insurance (for employees working 20+ hours per week)
• MetLife Voluntary Benefits
• Employee Assistance Program (EAP)
• 401K Retirement Savings Plan
• Direct Deposit & weekly epayroll
• Referral Bonus Programs
• Certification and training opportunities

Education

Bachelor’s degree in Computer Science, Information Systems, or Engineering

Compensation

Estimated rate: USD 65.00 to 80.00 per hour

Note

Any pay ranges displayed are estimations. Actual pay is determined by an applicant's experience, technical expertise, and other qualifications as listed in the job description.