Data Engineer III

Intelliswift$83K — $88K *
Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • 5-7 years of experience in data engineering using Informatica workflows
  • Strong proficiency in AWS data engineering tools (Glue, Lambda, EventBridge, S3)
  • Experience with Databricks and Snowflake
  • Bachelor's degree in Data Engineering, Computer Science, or related field
  • Understanding of business context behind data analytics across various domains like inventory and accounting
  • Familiarity with Apache Airflow is a plus
  • Excellent communication skills and energy in team dynamics

Responsibilities

  • Own and modernize data pipeline projects on the AWS platform
  • Collaborate with stakeholders to design solutions for diverse business needs
  • Partner with the Science team to implement AI-driven data solutions
  • Architect and implement next-generation BI and ETL processes
  • Refine existing data pipelines to enhance customer value
  • Drive operational excellence through automation and error reduction
  • Engage in learning and knowledge sharing within the broader engineering community

Benefits

  • Collaborative work environment with a focus on learning from industry leaders
  • Opportunity to work on innovative AI-based projects
  • Engagement in a culture of operational excellence and improvement
  • Onsite work setting fosters strong teamwork and collaboration
  • Participation in learning series and operational reviews with peers
Full Job Description
Pay rate range - $80/hr. to $85/hr.
100% Onsite

Must Have
Informatica workflow design and implementation required
AWS data engineering (Glue, Lambda, EventBridge Cradle, S3) highly preferred
Former Informatica exp, date bricks snowflake

Nice to Have
1. Apache Airflow
2. Someone who is fun and exciting with lots energy
3. Articulation

Degree/ Certification Requirements -
BIE/Data Engineering/ Computer Science

Background required -
Former Data Engineer who understands the business not just the data side (inventory to web traffic to accounting)

Job Description:
As a Data Engineer on the fashion clothing retailer team, you will routinely solve complex data problems, unblocking critical projects that drive fashion clothing retailer's mission to be "the daily destination for style inspiration and discovery.
" Whether you are optimizing analytics that help marketing tune their strategies, building new pipelines to speed up delivery, partnering with our science team to deliver AI solutions, or helping accounting drill down to track our business: your work will empower meaningful change for your customers.
You will partner closely with stakeholders across the business and with the Data Engineering team to deliver new features, or pitch in to migrate legacy features to a modern AWS-based platform.
You will be an active partner in the broader Client data engineering community (Client is fashion clothing retailer's parent company), taking part in learning series and operational reviews with industry-leading engineers.

Key job responsibilities
You will own projects that build new data pipelines, modernize existing ones onto our AWS-based platform, or refine pipelines to deliver more value for our customers.
You will partner with colleagues across fashion clothing retailer to understand their business domains and build solutions that meet their needs, including working with our Science team to bring AI solutions to production.
You will also be an active participant on the fashion clothing retailer data team, driving improvements to the team's operational health and reducing errors for customers. And you will take part in Client's engineering culture, learning from and teaching alongside the best, while making the most of AI-powered tools to work more effectively.

Some of your tasks will include:
• Architecting, designing, and implementing next-generation data pipelines and BI solutions built on AWS
• Building and optimizing ETL processes to improve data quality, reliability, and freshness
• Leveraging AI-powered developer and data tools to boost your own productivity and code quality
• Partnering with business stakeholders to translate their needs into scalable data solutions
• Collaborating with the Science team to build data foundations for AI solutions and bring them into production
• Improving the team's operational excellence through monitoring, automation, and error reduction

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