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Tier-1 employer, popular data engineer title, mid-level band, and metro location increase applicant competition.
Core data engineering skills transfer across industries, but Azure/Databricks specialization moderately narrows fit.
Explicit 3-8 years plus mandatory Azure/Databricks/Spark and DevOps/IaC requirements make filters strict.
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Design and implement scalable data pipelines and cloud-based data warehousing solutions using Microsoft Azure and Apache Spark/Databricks.
Provide technical and thought leadership on data ingestion, integration, modeling, and analytics within the Analytics Practice.
Collaborate with clients to translate business needs into technical specifications and deliver solutions iteratively using Agile/DevOps methodologies.
3 - 8 years of experience in data engineering with proven expertise in Azure Data Factory, Databricks, and Azure cloud services.
Strong hands-on experience with Apache Spark (Python and/or Scala) and building data pipelines, including API and streaming ingestion.
Bachelor’s degree in Engineering (BE/B.Tech), MCA, M.Tech, or MBA.
Mandatory skills: Azure Data Engineering (ADE), Azure Databricks (ADB), Azure Data Factory (ADF).
Experienced in designing and delivering enterprise-scale cloud data architecture and pipelines on Microsoft Azure.
Familiar with Agile delivery and DevOps processes including CI/CD and Infrastructure as Code.
Strong knowledge of data warehousing principles (Kimball methodology) with ability to lead technical discussions and practices.