Data Engineer

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We’re hiring a Data Engineer to design, build, and support reliable ELT/ETL pipelines on Microsoft Azure. You’ll partner with data architects, BI developers, and business stakeholders to land ERP and operational data into our lake/warehouse, model it for analytics, and keep it flowing. This role emphasizes implementation, optimization, and break/fix support over enterprise architecture ownership.

Key Responsibilities

• Build & operate pipelines using Azure Data Factory (or Synapse Data Pipelines) to ingest and transform data into our Azure Data Lake and Synapse/SQL layers.

• Develop SQL views/tables and reusable transformation logic to support analytics and reporting (including Power BI semantic models).

• Own reliability for assigned data domains: monitor jobs, triage incidents, perform root‑cause analysis, and execute break/fix and performance tuning.

• Ensure data quality via validation checks, schema enforcement, and automated tests; document lineage and business rules.

• Collaborate with BI developers and business partners to translate requirements into well-documented data sets optimized for consumption.

• Harden and improve CI/CD (Azure DevOps), environments, and runbooks for repeatable, compliant delivery.

• Contribute to standards (naming, partitioning, coding patterns) and knowledge base articles/playbooks for on-call support.

Minimum Qualifications

• 2–5 years in data engineering or adjacent software/data roles with hands-on delivery in cloud data platforms.

• Strong SQL and performance tuning on Synapse/SQL Server (or equivalent).

• Practical experience with Azure Data Factory / Synapse pipelines, Databricks (PySpark), and Azure Data Lake.

• Familiarity with dimensional/semantic modeling for Power Query or Power BI.

Preferred Qualifications

• Exposure to Microsoft Fabric concepts, Purview governance, or metadata/lineage tooling.

• Experience integrating data from ERP platforms (e.g., SAP, Sage/SL, Business One) and SaaS sources.

• Data testing frameworks, orchestration patterns, and cost/performance optimization on Azure.

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