Lead Data Engineer — Cloud Analytics & Platforms

McGraw Hill

Columbus (OH)

On-site

USD 115,000 - 150,000

Full time

5 days ago
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Job summary

McGraw Hill is seeking a Lead Data Engineer to enhance its data platform, delivering actionable insights across financial, product, and customer data streams. The role requires hands-on data engineering with AWS and Oracle technologies, strong data modeling skills, and collaboration with stakeholders to ensure timely delivery of large-scale data solutions.

You will design ETL/ELT pipelines, optimize cloud-based architectures, and ensure reliable performance of the data platform.

Qualifications

  • Bachelor's degree or equivalent experience.
  • Expertise in Data warehousing and modern data lake concepts.
  • 8+ years of experience in Data Engineering using tools such as Informatica/IICS, Oracle DB and Oracle packages.
  • 5+ years of experience working with Cloud like OCI, AWS, and Azure on Data technologies.

Responsibilities

  • Lead Data Engineer must have hands-on experience developing and delivering data solutions with AWS and/or Oracle technologies.
  • Work with data from financial and operational systems such as Oracle ERP Sales, Oracle DB and data modelling (SCD).
  • Run cloud platform with optimized solution architecture to meet daily runbook SLA.
  • Use Git and Jira for version control and project management in Agile/Kanban.
  • Design and build end-to-end data pipelines and data models for fast processing.

Skills

Informatica/IICS
Oracle DB
AWS
Databricks
Python
Scala
Java
Node
Unix shell
GIT
Jira

Education

Bachelor's degree or equivalent

Tools

GIT
Jira

Job description

McGraw Hill is seeking a Lead Data Engineer to enhance its data platform, delivering actionable insights across financial, product, and customer data streams. The role requires hands-on data engineering with AWS and Oracle technologies, strong data modeling skills, and collaboration with stakeholders to ensure timely delivery of large-scale data solutions.

You will design ETL/ELT pipelines, optimize cloud-based architectures, and ensure reliable performance of the data platform.

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