Data Engineer

IntePros

• $110K — $130K *
Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • 3-5 years of relevant Data Engineering experience.
  • Bachelor's degree in Data Engineering, Business Intelligence, Computer Science or a related field.
  • Proven track record with Informatica workflow design and implementation.
  • Experience optimizing complex data pipelines.
  • Strong problem identification skills and reliability improvement capabilities.
  • Effective communication skills to articulate business needs beyond technical specifications.

Responsibilities

  • Design, architect, and implement data pipelines and BI solutions using AWS.
  • Develop and implement Informatica workflows.
  • Build and optimize ETL processes for enhanced data quality and reliability.
  • Identify and simplify complex legacy data pipelines.
  • Transition existing data pipelines to AWS-based platforms.
  • Work with business stakeholders to create scalable data solutions.
  • Collaborate with the Science team to support AI development and production processes.

Benefits

  • Collaborative team environment with experienced Data Engineers and Business Intelligence professionals.
  • Onsite work location in New York City.
  • Standard working hours with opportunities for overtime for flexibility.
Full Job Description
Data Engineer
Overview
We are seeking a Data Engineer to build, optimize, and modernize data pipelines supporting a wide range of business functions, including operations, marketing, finance, accounting, and e-commerce. This role will focus heavily on improving the reliability and performance of an established Informatica environment while helping transition legacy data solutions to modern AWS-based platforms.
Core Responsibilities
  • Design, architect, and implement data pipelines and BI solutions using AWS.
  • Design and implement Informatica workflows.
  • Build and optimize ETL processes to improve data quality, reliability, and freshness.
  • Identify opportunities to simplify and improve complex legacy pipelines.
  • Modernize existing data pipelines onto AWS-based platforms.
  • Partner with business stakeholders to translate business needs into scalable data solutions.
  • Collaborate with the Science team to build data foundations for AI solutions and bring them into production.
  • Improve operational health through monitoring, automation, and error reduction.
  • Leverage AI-powered developer and data tools to improve productivity and code quality.
  • Participate in operational reviews and broader data engineering learning opportunities.
Required Qualifications
  • 3-5 years of Data Engineering or related experience.
  • Bachelor's degree in Data Engineering, Business Intelligence, Computer Science, or a related field.
  • Proven experience with Informatica workflow design and implementation.
  • Experience identifying and optimizing complex data pipelines.
  • Ability to proactively identify problems and improve pipeline reliability.
  • Strong communication and ability to understand business needs beyond the technical data environment.
Preferred Qualifications
  • AWS data engineering experience, particularly with Glue, Lambda, EventBridge, Cradle, and S3.
  • Experience with Databricks or Snowflake.
  • Apache Airflow experience.
  • Experience modernizing legacy data environments.
  • Ability to understand business processes across areas such as inventory, web traffic, operations, marketing, and accounting.
Top Skills
  • Informatica Workflow Design & Implementation
  • AWS Data Engineering
  • Data Pipeline Optimization & Reliability
Work Environment
  • Onsite in New York.
  • Collaborative team environment with three Data Engineers and one Business Intelligence Engineer.
  • Standard schedule is approximately 9:00 AM-5:00 PM.
  • Some overtime/shift coverage may be required approximately once per month.
Success in This Role
The successful candidate will be proactive in identifying problems within complex data pipelines and simplifying them where possible. They should be able to look beyond the data itself to understand the business processes the data supports and build reliable solutions that improve the experience for internal customers.

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