Data Engineer with Financial Services, Databricks and MDM Experience

Responsibilities

• Design, build, and sustain scalable data pipelines and enterprise data integration solutions.
• Implement batch and real time data ingestion, transformation, and processing frameworks.
• Develop cloud native data engineering solutions for data lake, data warehouse, and lakehouse architectures.
• Create 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 teams to enable enterprise data programs.
• Implement data quality checks, monitoring, metadata management, and data lineage practices.
• Assist cloud migration and modernization efforts for legacy and enterprise data platforms.
• Optimize data processing, storage, and pipelines for scalability and operational efficiency.
• Ensure adherence to security, governance, and regulatory standards within financial services environments.
• Support reporting, analytics, and downstream platforms through reliable data delivery.

Requirements

• Extensive hands on experience in data engineering and enterprise scale data integration.
• Proven track record delivering scalable ETL and ELT pipelines and distributed processing solutions.
• Experience with modern cloud based data platforms and ecosystems.
• Strong SQL proficiency plus programming or scripting in Python, PySpark, or Snowpark.
• dbt for data transformation and modeling
• dbt for ELT pipeline development within Snowflake or Databricks
• dbt for modular, reusable SQL based data workflows
• dbt for data testing, documentation, and version control integration
• Experience with 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, and Snowflake Tasks
• Background 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
• Experience with batch file-based ingestion
• Real time data streaming experience with technologies like Kafka or Spark Streaming

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

Must Haves

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

Education

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

Location

Jersey City, NJ (hybrid)

Compensation

• Hourly rate: USD 65 - 80
• Pay ranges are estimates and depend on experience, skills, and qualifications

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.