Data Engineer with Financial Services, Databricks and MDM Experience

Responsibilities

• Design, develop, and sustain scalable data pipelines and enterprise data integration solutions.
• Implement batch and streaming ingestion, transformation, and processing frameworks at scale.
• Create cloud-native data engineering solutions that support data lake, data warehouse, and lakehouse architectures across the enterprise.
• Develop ETL and ELT processes for structured, semi-structured, and unstructured data sources.
• Contribute to 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 initiatives.
• Enable data quality validation, monitoring, metadata governance, and data lineage tracking.
• Support 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

• 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.
• Strong SQL skills complemented by Python, PySpark, or Snowpark programming experience.
• dbt for data transformation and modeling, including ELT development within Snowflake and Databricks.
• dbt for modular, reusable SQL-based workflows, 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.
• 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, and batch file-based ingestion.
• Experience with real-time/streaming data technologies such as Kafka and 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 eligible employees
• Health Savings Account (HSA)
• Life and Disability Insurance
• MetLife Voluntary Benefits
• Employee Assistance Program (EAP)
• 401K Retirement Savings Plan
• Direct Deposit and 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 skills
• Oracle to Cloud migration experience
• Data quality and reconciliation capabilities

Education

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

Compensation

Estimated rate range: $65.00 to $80.00 per hour

What’s in it for you?

• Medical, Prescription, Dental & Vision benefits for eligible employees
• Health Savings Account (HSA)
• Life and Disability Insurance
• MetLife Voluntary Benefits
• Employee Assistance Program (EAP)
• 401K Retirement Savings Plan
• Direct Deposit and weekly ePayroll
• Referral Bonus Programs
• Certification and training opportunities

Note

Any pay ranges displayed are estimates. Actual compensation is determined by a candidate's experience, technical expertise, and other qualifications listed in the job description.