Data Engineer – Manufacturing Operations

Rwindia; Bengaluru; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

The Data Engineer – Manufacturing Operations at Rwindia is responsible for designing, building, and deploying scalable data engineering and analytics solutions in enterprise cloud environments. You’ll focus on data pipelines, cloud data architecture, big data processing, KPI reporting, and enabling data-driven decisions for manufacturing and operations.

Key responsibilities include designing data pipelines, ETL/ELT workflows, EDA, KPI frameworks, and BI visualizations using Power BI, Tableau,

Qualifications

• 4–7 years in Data Engineering or Analytics roles.
• Strong Azure data engineering concepts and cloud data solutions.
• Hands-on with Azure Databricks, Spark, Blob Storage, and SQL DW.
• Experience building scalable ETL/ELT pipelines and cloud data architecture.
• Proficiency with Snowflake and enterprise-scale data pipelines.
• Advanced SQL and Python programming for BI and analytics.
• BI visualization with Power BI and Tableau.
• Experience building CI/CD pipelines and deployment automation.
• Knowledge of Hadoop/Hive/HBase and Spark ecosystems.
• Consulting or client-facing experience is a plus.

Responsibilities

• Design, develop, and deploy scalable data engineering solutions in enterprise cloud environments.
• Build automated data pipelines from multiple sources and ensure data quality.
• Create robust ETL/ELT workflows and pipeline automation.
• Perform EDA to clean and validate data; identify patterns and insights.
• Develop KPI frameworks and data models for decision making.
• Design reporting and visualization using Power BI, Tableau, and Excel.

Skills

Experience 4–7y

Azure Data Eng

Azure Databricks

Spark

Blob Storage

SQL/Data Warehouse

Python

Power BI

Tableau

CI/CD pipelines

Big Data (Hadoop/Hive/HBase)

ADLS / cloud storage

Consulting experience

Education

Bachelor’s or Master’s degree in CS/Math/Stats

Tools

Azure Databricks

Spark

Blob Storage

Cool Blob Storage

Virtual Machines

Functions

SQL Data Warehouse

Snowflake

Job description

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.