Senior Analyst, Data Engineer

Lincoln Electric

21h•$112,000 - $140,000•Remote

About The Position

We are a $4B global manufacturing enterprise making significant investments in our cloud data and analytics platform. We’re seeking a Senior Analyst-Data Engineer to help design, build, and scale enterprise data pipelines that enable analytics, reporting, and operational insights across the business. This role sits within our core Analytics team and focuses on working with our Microsoft backbone. You will provision data from multiple enterprise systems into a Medallion Architecture, with hands‑on responsibility for Bronze and Silver layers. The work supports both business intelligence and advanced analytics use cases across the company including: manufacturing, supply chain, and operations. This position offers the chance to build and improve an accelerating deployment.

Requirements

3–5 years of professional experience as a Microsoft Data Engineer or similar role.
Hands‑on experience with Azure Synapse Analytics (SQL pools and/or Spark).
Experience building data pipelines with Azure Data Factory or equivalent tooling.
Practical experience implementing Medallion Architecture, specifically Bronze and Silver layers.
Strong SQL skills and experience using Spark / PySpark.
Experience working with Azure Data Lake Storage Gen2.

Nice To Haves

Experience with Microsoft Fabric (Lakehouse, Warehouses, Notebooks, Dataflows Gen2).
Manufacturing or industrial domain experience (ERP, MES, supply chain, operations data).
Exposure to tools such as Azure DevOps or GitHub Actions.
Experience working in a large, enterprise environment.

Responsibilities

Design, build, and maintain scalable data pipelines using Azure Synapse Analytics, Azure Data Factory, and/or Microsoft Fabric.
Ingest data from multiple enterprise sources (ERP, MES, operational databases, APIs, flat files, cloud sources) into the Bronze layer.
Transform and curate data into the Silver layer, applying standardization, cleansing, and enrichment logic.
Implement data quality checks, validation rules, and monitoring to ensure trusted, production‑ready datasets.
Develop and optimize SQL and Spark (PySpark / Spark SQL) workloads for performance and cost efficiency.
Collaborate closely with BI developers, analytics engineers, data scientists, and business stakeholders.
Follow enterprise standards for data security, governance, and access control.
Contribute to documentation, architectural patterns, and platform improvement initiatives.