Job Title: Data Engineer(python, pyspark, databricks)
Location: Dallas, TX
Data Engineer with strong proficiency in SQL, Python, and PySpark to support high-performance data pipelines and analytics initiatives. This role will focus on scalable data processing, transformation, and integration efforts that enable business insights, regulatory compliance, and operational efficiency.
Data Engineer SQL, Python and Pyspark Expert (Onsite Dallas, TX)
Key Responsibilities
Design, develop, and optimize ETL/ELT pipelines using SQL, Python, and PySpark for large-scale data environments
Implement scalable data processing workflows in distributed data platforms (e.g., Hadoop, Databricks, or Spark environments)
Partner with business stakeholders to understand and model mortgage lifecycle data (origination, underwriting, servicing, foreclosure, etc.)
Create and maintain data marts, views, and reusable data components to support downstream reporting and analytics
Ensure data quality, consistency, security, and lineage across all stages of data processing
Assist in data migration and modernization efforts to cloud-based data warehouses (e.g., Snowflake, Azure Synapse, GCP BigQuery)
Document data flows, logic, and transformation rules
Troubleshoot performance and quality issues in batch and real-time pipelines
Support compliance-related reporting (e.g., HMDA, CFPB)
Required Qualifications
6+ years of experience in data engineering or data development
Advanced expertise in SQL (joins, CTEs, optimization, partitioning, etc.)
Strong hands-on skills in Python for scripting, data wrangling, and automation
Proficient in PySpark for building distributed data pipelines and processing large volumes of structured/unstructured data
Experience working with mortgage banking data sets and domain knowledge is highly preferred
Strong understanding of data modeling (dimensional, normalized, star schema)
Experience with cloud-based platforms (e.g., Azure Databricks , AWS EMR , GCP Dataproc )
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