Data Engineer

Intelliswift - An LTTS Company; New York (NY); On-site; USD 120,000 - 165,000; Full time

Benefits offered by this job

Someone who is fun and exciting with a

Articulation

Job summary

Intelliswift - An LTTS Company, based in New York, is seeking a Data Engineer to design and implement data pipelines and BI solutions on an AWS-based platform. You will collaborate with stakeholders across fashion clothing retailer’s business units and partner with the Science team to deploy AI solutions into production.

You will grow the data engineering foundation, solve complex data problems, and help accelerate analytics, marketing, and accounting insights while learning from a strong

Qualifications

• Former Data Engineer who understands the business, not just the data side.
• Experience with AWS-based data platforms is preferred.
• Ability to collaborate with stakeholders across the business.
• Experience building data pipelines and BI solutions.

Responsibilities

• Own projects that build new data pipelines, modernize pipelines on AWS-based platform.
• Collaborate with fashion clothing retailer teams to understand business domains and build solutions.
• Partner with the Science team to bring AI solutions to production.
• Improve the team's operational health and reduce errors for customers.
• Contribute to Client's engineering culture and leverage AI-powered tools.

Skills

Data pipelines

ETL processes

AWS

BI solutions

AI solutions

Data quality

Automation

Monitoring

Stakeholder collaboration

Production deployment

Job description

Informatica workflow design and implementation required

Nice to Have

• 2. Someone who is fun and exciting with lots energy
• 3. Articulation

Degree/ Certification Requirements -

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.

fashion clothing retailer is part of Client Fashion, but with a unique vibe and mission to serve fashion-oriented customers.

The data engineering team frequently collaborates with teams across the business, providing opportunities to learn about the fashion industry and e-commerce (and employee discounts).

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