Envision Technology Solutions

Databricks Engineer

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Databricks Engineer in Wilmington, DE, with a long-term contract exceeding 6 months. Key skills required include PySpark, ETL modernization, and data integration. Experience with Snowflake and data governance is essential.
🌎 - Country
United States
💱 - Currency
$ USD
-
💰 - Day rate
Unknown
-
🗓️ - Date
April 25, 2026
🕒 - Duration
More than 6 months
-
🏝️ - Location
On-site
-
📄 - Contract
Unknown
-
🔒 - Security
Unknown
-
📍 - Location detailed
Wilmington, DE
-
🧠 - Skills detailed
#Data Architecture #Scala #Deployment #Ab Initio #Snowflake #Data Lake #Batch #Strategy #Data Engineering #UAT (User Acceptance Testing) #PySpark #Documentation #Data Lineage #Data Integration #Migration #Data Processing #Monitoring #Data Governance #Cloud #Data Pipeline #Databricks #Spark (Apache Spark) #Data Integrity #"ETL (Extract #Transform #Load)" #Datasets
Role description
Job Title: Databricks Engineer (PySpark and Data Lake) Location: Wilmington, DE – 5 Days onsite role Long Term Project Job Description We are seeking a Data Engineer to lead the modernization of legacy ETL systems by migrating Ab Initio workflows to scalable, modular PySpark pipelines on Databricks. The role involves transforming complex data ecosystems into cloud-native architectures while ensuring data integrity, performance, and reliability. Key Responsibilities ETL Modernization & Development • Analyze and migrate legacy ETL workflows from Ab Initio to PySpark-based pipelines • Design and develop scalable data pipelines on Databricks • Refactor monolithic processes into modular, reusable components • Leverage existing enterprise datasets to avoid redundancy Data Integration & Processing • Build and maintain ETL/ELT pipelines integrating data from Snowflake and other sources • Process and publish enriched datasets for downstream applications • Support batch and near real-time data processing Data Lineage & Optimization • Create end-to-end data lineage and data flow diagrams • Identify redundancies and drive process consolidation and optimization • Ensure adherence to data governance and quality standards Testing & Validation • Develop unit, integration, and reconciliation frameworks • Perform dual-run comparisons with legacy systems • Validate outputs in UAT and pre-production environments Deployment & Operations • Support cutover and migration strategy from legacy systems • Decommission legacy workflows and optimize scheduling (e.g., Control-M) • Develop runbooks, monitoring, and operational documentation Collaboration • Work with data architects, analysts, and downstream application teams • Coordinate user acceptance testing (UAT/FAT) and stakeholder sign-offs