Levi Strauss & Co.

Fashion

DataEngineer

$1–1k Bengaluru, India FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid+ candidates.

The Brief

“Data Engineer at Levi Strauss & Co.. Skills: Data Engineering, Cloud Solutions, Data Pipelines, Data Modelling. Develop groundbreaking solutions. Develop data pipelines”

Industry & Context.

Fashion
Problems you'll solve

Problem solving at scale; Root cause analysis

What They're Looking For.

Must Have

10+ years of experience in data engineering, Experience with data warehousing concepts, Experience with dimensional modelling, Experience with ETL best practices, Proficiency in SQL, Proficiency in Python or Java, Hands-on experience with Spark, Hands-on experience with Flink, Hands-on experience with Hive, Experience with one major public cloud platform, Working knowledge in DBT, Working knowledge in PySpark, Working knowledge in Snowflake, Experience with data visualisation tools, Working knowledge of GitHub/Git Toolkit, Working knowledge of CI/CD best practices

Nice to Have

GCP experience is good to have, Familiarity with Agentic AI, Experience with Tableau, Experience with Looker

What You'll Do.

Develop groundbreaking solutions

Develop data pipelines

Perform data transformations

Deliver data products

Provide technical expertise

Provide operational support

Migrate existing workloads

Build advanced cloud solutions

Implement strategies to improve security

Implement strategies to reduce costs

Implement strategies to meet use targets

Design data pipelines

Implement data pipelines

Implement ETL processes

Deliver data products

Understand requirements

Develop tailored solutions

Implement data quality

Implement data governance

Implement best practices

Establish monitoring systems

Perform root cause analysis

Implement preventative solutions

Design efficient solutions

Translate requirements into technical solutions

Plan incremental delivery

How You'll Work.

Team & Collaboration

Cross-continental data community; E-commerce & Consumer Data Domain team; Analysts; Data Scientists; SREs; Other engineers; Technical team members; Non-technical team members

Process & Methodology

Agile, CI/CD

Full Job Description

Calling all originals: At Levi Strauss & Co., you can be yourself — and be part of something bigger. We’re a company of people who like to forge our own path and leave the world better than we found it. Who believe that what makes us different makes us stronger. So add your voice. Make an impact. Find your fit — and your future. Join our Data & AI Platform Engineering organisation and lead the charge in transforming the fashion industry. As a Data Engineer, you will develop groundbreaking solutions that support our global business forward while being part of a dynamic, cross-continental data community. You will be an important member of the E-commerce & Consumer Data Domain team. You will work on a cloud native and modern data platform, building data pipelines to building data models and transformations to delivering data products and dashboards to our partners. You will provide technical expertise and operational support to meet the data needs for our businesses. From migrating existing workloads to building advanced cloud solutions, you will help shape and implement strategies to improve security, reduce costs, and meet use targets. You will collaborate with teams including Analysts, Data Scientists, SREs, and other engineers, focusing on delivering high-quality data products that foster a culture of engineering excellence. We are looking for someone who brings thoughtful perspective, creativity, and someone who can solve problems at scale. **About the Job** *Design and implement data pipelines and ETL processes to support large-scale data products. *Develop and deliver efficient data models and products that align with our needs. *Understand requirements and come up with data models and products while developing solutions tailored to analytical and usage requirements. *Implement data quality, governance, and best practices to ensure consistency, accuracy, and reliability across data products. *Establish monitoring systems to keep partner trust throughout the data

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