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This role is pivotal in designing, developing, and maintaining scalable data infrastructure and pipelines that enable our data initiatives to thrive.\nThe Data Developer will be responsible for implementing and optimizing data architectures, ensuring data quality and reliability, and collaborating closely with analytics and business teams to deliver high-performance data solutions. This position requires strong technical expertise in data engineering, cloud platforms, and modern data stack technologies, combined with the ability to translate business requirements into effective technical solutions.\nResponsibilities:\nData Pipeline Development: Build, optimize, and maintain scalable data pipelines and ETL/ELT processes to support analytics and business intelligence needs.\nData Architecture Implementation: implement data architectures, including data lakes, warehouses, and integration layers that support the organization's data strategy and modernization.\nData Quality & Governance: Implement data quality checks, validation processes, and monitoring systems to ensure data accuracy, consistency, and reliability.\nIntegration & API Development: Develop and maintain integrations between various data sources, third-party tools, and analytics platforms.\nCollaboration: Work closely with Data & Analytics Managers and business stakeholders to understand requirements and deliver technical solutions that enable data-driven decision making.\nPerformance Optimization: Monitor and optimize data pipeline performance, query efficiency, and storage costs to ensure scalable and cost-effective solutions.\nDocumentation: Create and maintain comprehensive technical documentation for data architectures, pipelines, and processes.\nData Security: Implement security best practices and ensure compliance with data privacy regulations across all data systems.\nPowered by AI: Leverage AI tools and automation to enhance data engineering workflows and productivity.\nRequirements:\nHands-on Databricks migration with the databricks/migrate tool (notebooks,jobs, clusters, secrets, ACLs), Unity catalog to Glue Catalog\n\nNotebook and job migration: notebooks, DAG pipelines, DLT pipelines\nStorage path refactoring: /mnt/ DBFS references to S3-backed locations\nData migration execution: Deltalake format to Iceberg format on S3 (Delta tables, raw files), incremental sync\nStreaming: Auto Loader / DLT checkpoint re-bootstrap, event_id dedup\nValidation: row counts, checksums, sample queries, ETL runtime comparison\nSecrets: recreate secret scopes in AWS Secrets Manager\nAWS services: S3, Glue, Secrets Manager, IAM basics, Athena, LakeFormation andEMR\n\nLanguages: Spark SQL, PySpark, Java, Python and Scala\nAgile pod delivery: daily ticket updates, standups, code review\nNice to Have:\nDatabricks Certified Consultant (Associate/Professional); 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