Job Description
Must Have Technical/Functional Skills
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- 10+ years of experience in Data Engineering and Data Architecture.
- 5+ years of hands-on experience with Databricks, PySpark, and Apache Spark.
- Strong expertise in - Databricks Lakehouse Architecture, Delta Lake, Unity Catalog, Delta Live Tables (DLT), Databricks Workflows, Medallion Architecture
- Experience designing enterprise-scale analytics and data platforms.
- Expert-level proficiency in PySpark and Spark SQL.
- Advanced SQL development and query optimization skills.
- Strong understanding of batch and streaming architectures.
- Experience with data warehousing, ETL/ELT, and large-scale data processing.
- Expertise in dimensional, relational, and analytical data modeling.
- Experience implementing data governance, data quality, and metadata management solutions.
- Knowledge of CI/CD practices, Git, DevOps, and Infrastructure as Code (Terraform preferred).
- Experience establishing architectural standards and engineering best practices.
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Roles & Responsibilities
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- Architect and design scalable, secure, and high-performance data platforms using Databricks and AWS.
- Lead solution design, data modeling, and enterprise data architecture initiatives.
- Design and optimize batch and real-time data pipelines using Databricks, PySpark, and Delta Lake.
- Partner with business and technology stakeholders to define requirements and deliver strategic data solutions.
- Establish standards for data governance, security, quality, performance, and operational excellence.
- Provide technical leadership, mentoring, and architectural guidance to engineering teams.
- Drive cloud modernization, platform optimization, and data transformation initiatives.
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