Data Engineer – Manufacturing Operations

Job Summary

The Data Engineer – Manufacturing Operations is responsible for designing, building, and deploying scalable data engineering and analytics solutions in enterprise cloud environments. The role focuses on building data pipelines, cloud-based data architecture, big data processing, business intelligence solutions, KPI reporting, and enabling data-driven decision-making for manufacturing and operational business functions.

Key Responsibilities

• Design, develop, and deploy scalable data engineering solutions in enterprise cloud environments.
• Build automated data pipelines for extracting data from multiple primary and secondary sources.
• Perform data engineering activities to create robust ETL/ELT workflows and pipeline automation.
• Conduct exploratory data analysis (EDA) to clean, validate, and improve data quality.
• Analyze large and complex datasets to identify patterns, trends, and business insights.
• Build data models and develop KPI frameworks to support decision-making processes.
• Design and manage reporting and visualization solutions using Power BI, Tableau, and Excel.
• Develop advanced visualizations and reporting logic using DAX and Python programming.
• Build scalable data architecture on Snowflake to support dashboards, analytics, and machine learning solutions.
• Work on Azure cloud technologies to build enterprise-grade data engineering platforms.
• Manage CI/CD pipelines for data integration and deployment automation.
• Support business users by building and managing data products and business intelligence solutions.
• Collaborate with stakeholders to understand business requirements and provide data-driven consulting solutions.

Required Skills & Experience

• 4–7 years of experience in Data Engineering or Analytics roles.
• Strong expertise in Azure Data Engineering concepts and cloud-based data solutions.
• Hands-on experience with Azure Databricks, Spark, Blob Storage, Cool Blob Storage, Virtual Machines, Functions, and SQL Data Warehouse.
• Strong experience building scalable ETL/ELT data pipelines and cloud data architecture.
• Experience with Snowflake platform and enterprise-scale data pipelines.
• Strong understanding of database warehousing principles and data modeling techniques.
• Advanced SQL and database management expertise.
• Strong programming skills in Python.
• Experience with Business Intelligence and visualization tools including Power BI and Tableau.
• Experience building CI/CD pipelines and deployment automation.
• Knowledge of Big Data technologies including Hadoop, Hive, HBase, and Spark.
• Experience in data analysis, reporting, and dashboard development.
• Strong understanding of cloud storage and data integration concepts such as ADLS.
• Proven experience working in consulting or client-facing environments.

Preferred Qualifications

• Bachelor’s or Master’s degree in Computer Science, Mathematics, Physics, Statistics, or related field.
• Experience working with machine learning data pipelines and analytics platforms.
• Familiarity with Agile development methodologies.
• Experience working on enterprise-scale manufacturing or operational analytics projects.

Other Requirements

• Strong analytical and problem-solving mindset.
• Ability to work independently and drive results proactively.
• Strong communication and presentation skills.
• Ability to collaborate effectively in team-driven environments.