Data Platform Architect - Azure, Databricks, SQL & Python

Phaxis; New York (NY); On-site; USD 140,000 - 190,000; Full time

Job summary

Phaxis is seeking a Data Architect to design scalable data solutions using Azure, Databricks, SQL, and Python. You will lead the migration of legacy SSIS packages and ETL workflows to Databricks, overseeing assessment, redesign, development, testing, and deployment.

Responsibilities include building complex data pipelines, writing optimized SQL, and collaborating with cross-functional teams to establish data governance, security, and performance standards for enterprise workloads.

Qualifications

• Strong experience designing scalable data architectures.
• Experience migrating legacy ETL (SSIS) to modern Databricks workflows.
• Proficiency in SQL querying, data modeling, and data pipelines.

Responsibilities

• Design scalable data architecture solutions using Azure, Databricks, SQL, and Python.
• Lead migration of SSIS packages to Databricks including assessment, redesign, development, testing and deployment.
• Develop and optimize SQL queries, pipelines, transformations, and data models for enterprise workloads.
• Build Databricks solutions using Python/PySpark, Delta Lake, notebooks, workflows, and Azure data services.
• Collaborate with engineering, analytics, and business teams to define data integration, governance, security, and performance standards.
• Troubleshoot and optimize migrated workloads to ensure data quality, scalability, reliability, and performance.

Skills

Azure

Databricks

SQL

Python

SSIS

ETL

Data architecture

Databricks Notebooks

Delta Lake

PySpark

Tools

PySpark

Delta Lake

Databricks Notebooks

Job description

Phaxis is seeking a Data Architect to design scalable data solutions using Azure, Databricks, SQL, and Python. You will lead the migration of legacy SSIS packages and ETL workflows to Databricks, overseeing assessment, redesign, development, testing, and deployment.

Responsibilities include building complex data pipelines, writing optimized SQL, and collaborating with cross-functional teams to establish data governance, security, and performance standards for enterprise workloads.