Senior Databricks Developer (AWS)

Project overview

Senior Databricks Developer (AWS)

We are seeking a Senior Databricks Developer with 7+ years of Data Engineering experience and deep expertise in Databricks, AWS, PySpark, Python, and SQL. This is a highly technical, hands-on engineering role within a data engineering team focused primarily on building, enhancing, and supporting enterprise data pipelines. The ideal candidate will have extensive experience developing cloud-native data solutions in AWS, implementing Delta Lake and Medallion architectures, and delivering scalable, production-grade data pipelines using Databricks.

This is not a consulting role; we are looking for a developer who enjoys building and optimizing data platforms and solving complex data engineering challenges.

Responsibilities

Required Qualifications

Preferred Qualifications

Key Skills

Databricks, AWS, PySpark, Python, SQL, Delta Lake, Medallion Architecture, Lakehouse Architecture, ETL/ELT Pipelines, Spark Optimization, Delta Live Tables (DLT), Databricks Workflows, Unity Catalog, S3, Glue, Lambda, EMR, Kinesis, Redshift, CloudWatch, Kafka, Airflow, GitHub, CI/CD, Terraform.

Job Facts: Contract W2 | 12 Months | Travel Required | Able to Sponsor | Hybrid | $50+

Skills: Amazon Kinesis, Amazon Redshift, Amazon S3, Amazon Web Services, Data Quality, Data Warehouse, Continuous Integration, Data Engineering, Data Modeling, Data Processing, Analytics, Apache Kafka, Electronic Health Record (EHR), Extract, Transform, Load, FOCUS, GitHub, Apache Spark, Cloud Computing, Optimization, Continuous Delivery, Databricks, PySpark, Python, DevOps, ELT, Streaming, Terraform, Jenkins, Microsoft Azure, Orchestration, Performance Tuning, SQL, Scalability, Unity, Unstructured Data, Workflow

Reference: Dice Id: 91103449 | Position Id: 9138799

• Design, develop, and maintain large-scale ETL/ELT pipelines using Databricks, PySpark, Python, and SQL.
• Build scalable and highly reliable data ingestion, transformation, and orchestration frameworks within AWS.
• Develop and support Delta Lake implementations and Medallion Architecture (Bronze, Silver, Gold) data pipelines.
• Create and optimize batch and streaming pipelines that process large volumes of structured and unstructured data.
• Leverage AWS services including S3, Glue, Lambda, EMR, IAM, EventBridge, CloudWatch, Kinesis, and Redshift to support end-to-end data workflows.
• Optimize Spark jobs, Delta tables, and Databricks workloads for performance, scalability, and cost efficiency.
• Partner with Data Engineers, Architects, Analytics teams, and business stakeholders to deliver high-quality data solutions.
• Implement data quality controls, monitoring, alerting, and operational support processes.
• Troubleshoot and resolve complex production issues across Databricks and AWS environments.
• Contribute to engineering best practices, code reviews, automation, and platform modernization initiatives.
• 7+ years of Data Engineering experience building enterprise-scale data platforms and pipelines.
• 5+ years of hands-on Databricks development experience in production environments.
• Strong expertise in Python, PySpark, and SQL with a focus on pipeline development.
• Extensive experience developing and supporting data pipelines within AWS.
• Strong knowledge of Delta Lake, Lakehouse Architecture, and Medallion Architecture (Bronze, Silver, Gold).
• Hands-on experience with AWS services such as S3, Glue, Lambda, EMR, Redshift, Kinesis, IAM, and CloudWatch.
• Deep understanding of Spark architecture, distributed data processing, performance tuning, partitioning, and optimization.
• Experience building highly scalable batch and streaming data processing solutions.
• Strong understanding of data modeling, data warehousing concepts, and modern data architectures.
• Experience supporting production environments, monitoring data pipelines, and troubleshooting complex data issues.
• Experience with Databricks Workflows, Delta Live Tables (DLT), Unity Catalog, and Structured Streaming.
• Experience with Kafka, Airflow, or cloud-native orchestration frameworks.
• Experience implementing CI/CD pipelines using GitHub Actions, Jenkins, or Azure DevOps.
• Infrastructure as Code experience utilizing Terraform or CloudFormation.
• Databricks Certified Data Engineer Associate or Professional certification.
• Experience working within large enterprise AWS environments supporting mission-critical data platforms.

Project details

• Start: flexible
• Duration: 12 Months
• Remote: hybrid
• Location: Malvern, PA, US, US
• Languages: English