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# Sr Data Engineer with Python & Databricks

- **Company:** Phaxis LLC
- **Location:** Edison, NJ, United States
- **Experience:** Expert
- **Salary:** $140,000 - $170,000
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Airflow, Amazon Web Services, Amazon S3, Microsoft Azure, Big Data, BigQuery, Information Engineering, Data Integration, Extract Transform Load (ETL), Dataspaces, Data Systems, Data Warehousing, DevOps, Distributed Computing Environment, Electronic Data Interchange (EDI), Python (Programming Language), Performance Tuning, SQL Databases, Data Streaming, Data Processing, Enterprise Software Applications, Data Storage Technologies, Snowflake, Apache Spark, Reliability of Systems, Data Lakes, Pyspark, Information Technology, Deployment Automation, Data Pipelines, Serverless Computing, Databricks
- **Published:** November 7, 2026
- **Apply:** https://www.thejobnetwork.com/job/0a08333d-f369-4403-9a85-6aba6c8faf2c/sr-data-engineer-with-python-databricks

## About the Role

* Bachelor's or Master's degree in Computer Science, Data Engineering, or a related technical field.
 * Advanced proficiency in Python and SQL for data manipulation, transformation, and automation.
 * Deep experience with Databricks, including Spark optimization, Delta Lake management, job orchestration, and workspace administration.
 * Strong understanding of distributed data processing, partitioning strategies, and performance tuning in Databricks and Spark.
 * Demonstrated experience designing and implementing enterprise-scale data integrations across Databricks, cloud platforms (AWS, Azure, or GCP), enterprise applications, APIs, and modern data ecosystems.
 * Hands-on experience with cloud platforms (AWS, Azure, or GCP) and their data ecosystems (e.g., S3, ADLS, BigQuery, Snowflake).

## Description

This role focuses on building efficient data pipelines, implementing best practices in data engineering, and ensuring system reliability and performance. The Data Engineer works closely with data scientists, analysts, and software engineers to deliver optimized data solutions for both batch and real-time processing., * Design, develop, and maintain end-to-end data pipelines and ETL/ELT workflows using Python, SQL, and modern orchestration tools (e.g., Airflow, dbt).

 * Architect and optimize data storage solutions, including data lakes and data warehouses, using Databricks, Delta Lake, and cloud-native services.
 * Build scalable data processing solutions leveraging Databricks notebooks, jobs, and clusters for both batch and streaming data workloads.
 * Develop and manage Databricks workflows using Spark (PySpark, SQL, or Scala) to transform, cleanse, and aggregate large datasets.
 * Design, develop, and implement complex data integrations across Databricks, cloud platforms (AWS, Azure, or GCP), enterprise applications, APIs, and modern data ecosystems to enable reliable, scalable data exchange and processing.
 * Implement data quality checks, schema validation, and monitoring to ensure data accuracy and reliability.
 * Optimize Databricks cluster configurations and job performance to minimize cost and maximize throughput.
 * Collaborate with DevOps teams to automate deployments, CI/CD pipelines, and infrastructure-as-code (IaC) for data systems.