Senior Data Engineer

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Data Engineering & Pipeline Development <\/span><\/b>
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• Design and implement data ingestion patterns into Databricks using Delta Lake, optimizing <\/b> for large\-scale data processing and storage. <\/span><\/span>
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• Develop, optimize, and troubleshoot complex Spark jobs (PySpark\/Scala)<\/b> for data processing and transformation within Databricks. <\/span>
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• Manage and extend data ingestion pipelines using Airbyte <\/b> (or similar <\/span> modern tools like Fivetran, Stitch), including configuring connectors, monitoring syncs, and ensuring data quality and reliability from diverse source systems (e.g., ERP, CRM, marketing, supply chain). <\/span>
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• Orchestrate and automate data pipelines and dbt models using Databricks Workflows and potentially integrating with other orchestration tools. <\/span>
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  Data Modeling & Collaboration <\/span><\/b>
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  • Collaborate with Analytics Engineers to translate business requirements into efficient and scalable data models using dbt <\/b> (Data Build Tool). <\/span>
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  • Implement dbt best practices for modularity, testing, documentation, and version control, ensuring seamless integration with Databricks. <\/span>
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  • Partner effectively with Analytics Engineers, Data Scientists, and business stakeholders to deliver high\-quality data solutions. <\/span>
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  • Provide technical guidance and mentorship to junior team members, and champion data engineering best practices, code quality, and documentation standards. <\/span>
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    Qualifications & Skills <\/span><\/b>
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    • Education: <\/span><\/b> Bachelor's degree in Computer Science, Data Engineering, or a related technical field required. <\/span>
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    • Experience: <\/span><\/b> 5+ years of progressive experience as a Data Engineer, with a strong focus on cloud\-based data platforms. <\/span>
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    • Deep Databricks Expertise: <\/span><\/b> Extensive experience with Spark (PySpark\/Scala), Delta Lake, Unity Catalog, Databricks SQL, and platform administration. <\/span>
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    • Data Modeling: <\/span><\/b> Proven experience with dbt <\/b> for data modeling, transformation, and testing. <\/span>
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    • Infrastructure as Code (IaC): <\/span><\/b> Strong proficiency with Terraform <\/b> for defining, provisioning, and managing cloud infrastructure and Databricks resources as code. <\/span>
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    • DevOps & CI\/CD: <\/span><\/b> Expertise in Git and GitHub Actions <\/b> for version control and implementing robust CI\/CD pipelines. <\/span>
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    • Programming: <\/span><\/b> Proficiency in SQL <\/b> and at least one programming language (Python strongly preferred, Scala is a plus). <\/span>
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    • Data Architecture: <\/span><\/b> Solid understanding of data warehousing, data lake, and lakehouse architectures. <\/span>
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