Data Engineer II - AI & Data Pipelines

Deloitte France; Tulsa (OK); On-site; USD 71,000 - 141,000; Full time

Job summary

Deloitte's AI & Data offering delivers end‑to‑end data and AI platforms, enabling clients to design, deploy, and operate data solutions that win on performance and scale. The PROJECT - Data Engineer II will contribute to reporting, analytics, and AI use cases by building robust data pipelines and collaborating with cross‑functional teams.

The role emphasizes quality, communication, and the ability to manage multiple tasks in a fast‑paced environment, with preferred experience in Spark,

Qualifications

• Bachelor's degree in a quantitative field (CS/IS/Engineering/Math/Statistics).
• 0–2 years in Agile delivery environments.
• 1+ years in ETL architecture and development.
• 1+ years of Python and SQL for data pipelines.
• 1+ years with relational DBMS.
• Ability to travel 0–10% on average.
• Authorized to work in the United States without sponsorship.

Responsibilities

• Design, develop, and maintain batch and streaming data pipelines for reporting, analytics, and AI use cases.
• Build and optimize ETL processes using SQL, Python, and data engineering frameworks.
• Integrate data from structured and unstructured sources across cloud and on‑prem environments.
• Support data quality, validation, monitoring, and issue resolution for production data assets.
• Collaborate with cross‑functional teams to implement data models and improve platform performance.

Skills

Python

SQL

ETL

Data pipelines

Communication

Teamwork

Deadline management

Project leadership

Education

Bachelor's degree in Computer Science or related field

Tools

Apache Spark

Databricks

Snowflake

Apache Airflow

Docker

Azure SQL Server/Power Platform

Job description

Deloitte's AI & Data offering delivers end‑to‑end data and AI platforms, enabling clients to design, deploy, and operate data solutions that win on performance and scale. The PROJECT - Data Engineer II will contribute to reporting, analytics, and AI use cases by building robust data pipelines and collaborating with cross‑functional teams.

The role emphasizes quality, communication, and the ability to manage multiple tasks in a fast‑paced environment, with preferred experience in Spark,