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The ideal candidate will have experience leading the design and implementation of enterprise-scale data solutions across on-premise and cloud platforms. You will be responsible for architecting and implementing ETL / ELT pipelines using Azure Data Factory, AWS Glue, SSIS, or similar technologies, and ensuring seamless data integration across various structured, semi-structured, and unstructured data sources.
Key Responsibilities:
• Design, develop, and optimize scalable, high-availability data architectures across on-premise (SQL Server, SSIS, SSRS, SSAS) and cloud platforms (Azure, AWS, Databricks, Snowflake, BigQuery, Synapse Analytics).
• Architect and implement ETL / ELT pipelines using Azure Data Factory, AWS Glue, SSIS, or similar technologies.
• Enable real-time data streaming using Kafka, Event Hubs, or Kinesis.
• Ensure seamless data integration across various structured, semi-structured, and unstructured data sources.
• Define best practices for data storage, performance tuning, indexing, and partitioning.
Data Modeling & Warehousing:
• Design enterprise data models (OLTP & OLAP) for high-performance analytics.
• Architect data lakes, data warehouses, and data marts on Azure Synapse, Snowflake, AWS Redshift, Databricks, or similar platforms.
• Optimize dimensional modeling, star / snowflake schemas, and data partitioning strategies.
• Build and manage data platforms preferably on Azure (Synapse, Databricks, Data Factory, Data Lake), AWS (Redshift, Glue, S3, Athena), or GCP (BigQuery, Dataflow).
• Implement serverless data solutions (AWS Lambda, Azure Functions) for cost-effective processing.
Requirements:
• Experience: 10+ years in Data-related roles | 5+ years as a Data Architect
• Strong experience with SQL Server, PostgreSQL, MySQL, NoSQL (CosmosDB, MongoDB, Cassandra).
• Proficiency in ETL / ELT pipelines using SSIS, ADF, dbt, Glue.
• Strong programming skills in Python, SQL, Scala (nice to have), PowerShell.
• Hands-on experience in data security, data lineage, data governance frameworks.
• Excellent understanding of data modeling, indexing, partitioning, and query optimization.
Career Development:
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