Azure Databricks Lead/Architect - Modern Data

Overview

On Site
Depends on Experience
Contract - W2
Contract - Independent
Contract - 12 Month(s)

Skills

Azure
Databricks
PySpark
modern data platforms
cloud-native
data governance
Spark
Hadoop
data warehouse
Data Engineering

Job Details

Job Title: Azure Databricks Lead/Architect - Modern Data

Location : Detroit MI Onsite

Duration : 24+ Months

Domain : Property and Casualty (P&C) is preferred

Responsibilities:

  • Develop modern data solutions and architecture for cloud-native data platforms.
  • Build cost-effective infrastructure in Databricks and orchestrate workflows using Databricks/ADF.
  • Lead data strategy sessions focused on scalability, performance, and flexibility.
  • Collaborate with customers to implement solutions for data modernization.
  • Create training plans and learning materials to upskill VM associates.
  • Build a smart operations framework for DataOps and MLOps.

Requirements

  • Should have 14+ years of experience with last 4 years in implementing Cloud native end-to-end Data Solutions in Databricks from ingestion to consumption to support variety of needs such as Modern Data warehouse, BI, Insights and Analytics
  • Should have experience in architecture and implementing End to End Modern Data Solutions using Azure and advance data processing frameworks like Databricks etc.
  • Experience with Databricks, PySpark, and modern data platforms.
  • Proficiency in cloud-native architecture and data governance.
  • Strong experience in migrating from on-premises to cloud solutions (Spark, Hadoop to Databricks).
  • Understanding of Agile/Scrum methodologies.
  • Demonstrated knowledge of data warehouse concepts. Strong understanding of Cloud native databases, columnar database architectures
  • Ability to work with Data Engineering teams, Data Management Team, BI and Analytics in a complex development IT environment.
  • Good appreciation and at least one implementation experience on processing substrates in Data Engineering - such as ETL Tools, Kafka, ELT techniques
  • Data Mesh and Data Products designing, and implementation knowledge will be an added advantage.
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