Data Engineer

Looking for:

-Financial services experience
-Data pipeline development
-Databricks/Delta Lake
-Master Data Management (MDM)(Golden Source) integration
-Strong SQL and data engineering skills
-Oracle to Cloud migration experience
-Data quality and reconciliation
- Strong hands-on experience in Data Engineering and enterprise-scale data integration.
-Proven experience developing scalable ETL/ELT pipelines and distributed data processing solutions.
-Experience working with modern cloud-based data platforms and data ecosystems.
-Hands-on expertise with Strong SQL expertise along with programming/scripting experience in Python PySpark or Snowpark.

Job Description

We are seeking a hands-on Data Engineer with strong experience in building scalable enterprise data solutions within Financial Services environments. The ideal candidate will have expertise in cloud-based data platforms modern data engineering practices and large-scale data integration initiatives supporting operational analytical and regulatory data needs.

This role requires strong technical capabilities in data pipeline development cloud data processing Master Data Management (MDM) and enterprise data integration. The candidate should be comfortable working across complex distributed environments and partnering with architecture analytics governance and business teams to deliver reliable secure and scalable data solutions.

Key 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 supporting enterprise data lake warehouse and lakehouse platforms.

Implement ETL/ELT processes for structured semi-structured and unstructured data sources.

Support Master Data Management (MDM) initiatives across security account client and reference data domains.

Collaborate with data architects business analysts governance teams and application teams to support enterprise data initiatives.

Implement data quality validation monitoring metadata management and lineage processes.

Support cloud migration and modernization efforts involving legacy and enterprise data platforms.

Optimize data processing storage and pipeline performance for scalability and operational efficiency.

Ensure compliance with enterprise security governance and regulatory standards within financial services environments.

Support reporting analytics and downstream consumption platforms through reliable and trusted data delivery.

Required Skills & Experience:

Strong hands-on experience in Data Engineering and enterprise-scale data integration.

Proven experience developing scalable ETL/ELT pipelines and distributed data processing solutions.

Experience working with modern cloud-based data platforms and data ecosystems.

Hands-on expertise with Strong SQL expertise along with programming/scripting experience in Python PySpark or Snowpark.

Experience with dbt (Data Build Tool) for:

Data transformation and modeling

ELT pipeline development within Snowflake/Databricks

Modular reusable SQL-based data workflows

Data testing documentation and version control integration

Experience with cloud platforms such as Azure AWS or GCP including integration with Snowflake and Databricks.

Solid understanding of data lake data warehouse and lakehouse architectures and their implementation across platforms.

Experience with orchestration and workflow tools (e.g. Airflow Databricks Workflows Snowflake Tasks) for pipeline scheduling and automation.

Experience supporting Master Data Management (MDM) and enterprise data governance initiatives.