Databricks Data Engineer

Machine Learning Technologies, LLC; Dallas (TX); On-site; USD 150,000 - 190,000; Full time

Job summary

Machine Learning Technologies, LLC seeks a Senior Databricks Data Engineer to design and optimize large-scale data pipelines on the Databricks platform in a Dallas-area setting. You will build end-to-end ETL/ELT processes, integrate with AWS S3, Snowflake, Airflow, and Glue, and deliver enterprise-grade analytics capabilities.

The role emphasizes scalable data architectures, data quality, governance, and collaboration with analytics teams to support regulatory and business insights.

Qualifications

• 7+ years hands-on experience with Databricks and modern data engineering platforms.
• Strong expertise in PySpark, Spark SQL and Apache Spark.
• Experience building ETL/ELT pipelines on cloud platforms.
• Experience with AWS S3, Snowflake, Apache Airflow and AWS Glue.
• Knowledge of data modeling, data warehousing, and big data processing.
• Experience with JSON, Parquet, Avro and CSV formats.
• Hands-on data quality frameworks, monitoring and governance.

Responsibilities

• Design, develop, and maintain scalable data solutions on Databricks using PySpark and Spark SQL.
• Build end-to-end ETL/ELT pipelines integrating Databricks with AWS services like S3, Glue, and Snowflake.
• Transform large-scale structured and semi-structured data for reporting and regulatory needs.
• Develop reusable data processing frameworks for ingestion, cleansing, validation, and enrichment.
• Tune Spark applications and Databricks workloads for performance and cost efficiency.
• Collaborate with stakeholders to translate data requirements into scalable platforms.
• Troubleshoot production pipelines and ensure high availability and reliability.
• Support analytics initiatives including segmentation, campaigns, and regulatory reporting.

Skills

Databricks

PySpark

Spark SQL

SQL

Data modeling

Tools

AWS

Airflow

Delta Lake

Unity Catalog

Snowflake

AWS Glue

Job description

Work Authorization:

We are seeking an experienced Senior Databricks Data Engineer with strong expertise in designing, developing, and optimizing large-scale data engineering solutions on the Databricks platform. The ideal candidate will have extensive hands-on experience in building enterprise-grade data pipelines, processing high-volume datasets, and delivering scalable analytics solutions using PySpark, Spark SQL, and AWS cloud technologies.

Key Responsibilities:

• Design, develop, and maintain scalable data engineering solutions using Databricks, PySpark, Spark SQL, and Apache Spark.
• Build and optimize end-to-end ETL/ELT pipelines integrating Databricks with AWS S3, Snowflake, Airflow, and AWS Glue.
• Process and transform large-scale structured and semi-structured datasets to support enterprise reporting, analytics, and regulatory requirements.
• Develop reusable frameworks and components for: Data ingestion Data cleansing and transformation Data validation and reconciliation Data enrichment Business rule implementation Implement robust data quality checks, monitoring, and governance controls to ensure data accuracy and reliability.
• Perform performance tuning and optimization of Spark applications and Databricks workloads to improve scalability and processing efficiency.
• Collaborate with business stakeholders, architects, and analytics teams to understand data requirements and deliver scalable solutions.
• Troubleshoot and resolve production data pipeline issues while ensuring high availability and reliability.
• Support advanced analytics, customer segmentation, campaign analytics, AML/regulatory reporting, and data validation initiatives.

Required Skills:

Technical Skills 7+ years of hands‑on experience with Databricks and modern data engineering platforms. Strong expertise in: PySpark Spark SQL Apache Spark Databricks Lakehouse Architecture Experience building and managing ETL/ELT pipelines on cloud platforms. Strong experience with: AWS S3 Snowflake Apache Airflow AWS Glue Knowledge of data modeling, data warehousing, and big data processing techniques. Experience working with structured and semi‑structured data formats such as JSON, Parquet, Avro, and CSV. Hands‑on experience implementing data quality frameworks and monitoring solutions. Strong SQL and data analysis skills. Preferred Skills Experience with regulatory reporting, AML, compliance, or financial data domains. Exposure to CI/CD and DevOps practices for data engineering. Experience with Delta Lake, Databricks Workflows, and Unity Catalog. Knowledge of Agile/Scrum delivery methodologies. Key Experience Areas Candidates should have demonstrated experience in one or more of the following: Customer Segmentation Analytics Campaign Analytics AML and Regulatory Reporting Enterprise Data Platforms Production Data Pipeline Support Data Validation and Reconciliation Large-Scale Data Migration and Modernization Programs.

We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law.