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Job Description:
This position will have strong hands-on experience in data platform architecture, cloud data engineering, and Databricks platform design and implementation. You will work closely with business stakeholders, data engineers, analysts, and technology teams to deliver enterprise-grade data solutions that support analytics, reporting, and advanced data use cases.
Key Responsibilities
- Design and implement scalable data platform architectures on Microsoft Azure.
- Lead the design, setup, and implementation of Databricks-based data platforms.
- Develop and optimize data processing frameworks using Databricks, Spark SQL, PySpark, and Python.
- Design, build, and maintain robust ETL/ELT pipelines using Azure Data Factory and related Azure data services.
- Define data lake, lakehouse, and data warehouse architecture patterns to support enterprise data needs.
- Implement data ingestion, transformation, orchestration, and publishing processes across structured and semi-structured data sources.
- Establish best practices for data platform development, deployment, monitoring, security, and performance optimization.
- Partner with business and technical teams to translate data requirements into scalable technical solutions.
- Optimize data pipelines and Spark workloads for performance, reliability, and cost efficiency.
- Support data governance, access control, data quality, metadata management, and platform security requirements.
- Create architecture documentation, technical design documents, standards, and operational guidelines.
- Provide technical guidance to data engineers and project teams on modern data platform practices.
Required Qualifications
- 5+ years of experience in data platform architecture, data engineering, cloud data solutions, or related roles.
- Strong experience with Microsoft Azure data services.
- Hands-on experience with Azure Data Factory for data integration, orchestration, and pipeline development.
- Proven experience in Databricks platform design, implementation, administration, and optimization.
- Strong programming skills in Python.
- Solid experience with Spark SQL, PySpark, and distributed data processing.
- Experience designing and implementing ETL/ELT pipelines for enterprise-scale data environments.
- Good understanding of data lake, lakehouse, and data warehouse architecture.
- Experience working with large-scale data processing, data modeling, and data integration patterns.
- Strong knowledge of data platform performance tuning, monitoring, and troubleshooting.
- Ability to communicate effectively with both technical and non-technical stakeholders.
- Strong analytical, problem-solving, and solution design skills.
Preferred Qualifications
- Experience with Delta Lake, Unity Catalog, Databricks Workflows, Auto Loader, and Lakehouse architecture.
- Experience with Azure Data Lake Storage, Azure Synapse Analytics, Azure Key Vault, Event Hub, or related Azure services.
- Familiarity with CI/CD, Git, DevOps practices, Terraform, or infrastructure-as-code.
- Experience with data governance, data quality frameworks, metadata management, and access control.
- Experience building enterprise data platforms from the ground up.
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