Senior Data Engineer

• →Assemble large, complex data sets that meet functional and non-functional business requirements.
• →Identify, design, and implement process improvements, including automation, data delivery optimization, and infrastructure redesign for scalability.
• →Lead and deliver data-driven solutions across multiple languages, tools, and technologies.
• →Develop and configure end-to-end data pipelines using Fivetran, ensuring efficient extraction, transformation, and loading (ETL) processes.
  Integration Setup: Connect multiple data sources (APIs, SaaS platforms, databases) to Fivetran and manage schema mapping and transformations.
• →Monitor pipeline performance, troubleshoot issues, and implement improvements for scalability and reliability.
• →Contribute to architecture discussions, solution design, and strategic technology adoption.
• →Build and optimize highly scalable data pipelines incorporating complex transformations and efficient code.
• →Design and develop new source system integrations from varied formats (files, database extracts, APIs).
• →Design and implement solutions for delivering data that meets SLA requirements.
• →Work with operations teams to resolve production issues related to the platform.
• →Apply best practices such as Agile methodologies, design thinking, and continuous deployment.
• →Develop tooling and automation to make deployments and production monitoring more repeatable.
• →Collaborate with business and technology partners, providing leadership, best practices, and coaching.
• →Mentor peers and junior engineers; educate colleagues on emerging industry trends and technologies.

• Bachelor’s degree in Computer Science, Software Engineering, or related field, or equivalent experience
• 7+ years of data engineering/development experience, including Python or Scala, SQL, and relational/non-relational data storage. (ETL frameworks, big data processing, NoSQL)
• 3+ years of experience in distributed data processing (Spark) and container orchestration (Kubernetes)
• Proficiency in data streaming in Kubernetes and Kafka
• Proven experience with Fivetran (pipeline setup, connectors, transformations)
• Experience with cloud platforms – Azure preferred; AWS or GCP also considered.
• Solid understanding of CI/CD principles and tools
• Familiarity with big data technologies such as Hadoop, Hive, HBase, Object Storage (ADLS/S3), Event Queues.
• Strong understanding of performance optimization techniques such as partitioning, clustering, and caching
• Proficiency with SQL, key-value datastores, and document stores
• Familiarity with data architecture and modeling concepts to support efficient data consumption
• Advanced understanding of data normalization and denormalization techniques.
• Translate enterprise requirements into effective data models and address project requirements when applicable.
• Design, build, and optimize scalable batch and streaming data pipelines using Databricks (Apache Spark, Delta Lake) to support Medalion Architecture.
• Automate workflows and maintain clear documentation for all data processes.
• Contribute to the design and operational management of a cloud-native data platform on Azure, integrating services such as Azure Data Lake Storage, Event Hubs, Azure SQL, and AKS.
• Monitor and troubleshoot data pipelines and platform workloads. Optimize Spark jobs, cluster configurations, and SQL warehouses to improve performance
• Strong collaboration and communication skills; ability to work across multiple teams and disciplines.

• Master’s degree in Computer Science, Software Engineering, or related field
• Knowledge of data governance, metadata management, or data quality/observability
• Familiarity with schema design and data contracts
• Experience handling various file formats (video, audio, image)
• Experience with Databricks, Snowflake, or similar platforms

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