Databricks Data Engineer

Ethereum Technologies; Dallas (TX); On-site; USD 120,000 - 170,000; Full time

Job summary

Ethereum Technologies is seeking an experienced Senior Databricks Data Engineer to design, develop, and optimize large-scale data pipelines on the Databricks platform. You will work with PySpark, Spark SQL, and AWS cloud technologies to deliver enterprise analytics and regulatory-compliant data solutions.

The role involves building end-to-end ETL/ELT pipelines, integrating with AWS S3, Snowflake, Airflow, and AWS Glue, and implementing data quality, governance, and monitoring frameworks.

Qualifications

• 7+ years of hands-on experience with Databricks and modern data engineering platforms.
• Strong expertise in PySpark and Spark SQL.
• Experience building and managing ETL/ELT pipelines on cloud platforms (AWS).
• Knowledge of data modeling, data warehousing, and big data processing techniques.
• Experience with JSON, Parquet, Avro, CSV formats.
• Experience with data quality frameworks and monitoring solutions.

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 for enterprise reporting and analytics.
• Develop reusable data ingestion, cleansing, transformation, validation, enrichment, and governance components.
• Tune Spark workloads to improve scalability and efficiency; troubleshoot production data pipelines and ensure high availability.

Skills

Databricks

PySpark

Spark SQL

ETL pipelines

AWS S3

Snowflake

Airflow

AWS Glue

Delta Lake

Databricks Workflows

Tools

AWS S3

Snowflake

Apache Airflow

AWS Glue

Delta Lake

Databricks Unity Catalog

Job description

Client:

To Be Discussed Later

Employment Type:

W-2, 1099, C2C

Work Authorization:

US Citizen, Green Card, H-1B, OPT-EAD, CPT, Other

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.