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# Data Engineer

- **Company:** Collabera
- **Location:** Atlanta, GA, United States
- **Experience:** Expert
- **Salary:** $124,800.0 - $249,600.0
- **Contract:** Temporary contract
- **Skills:** Java (Programming Language), Airflow, User Authentication, Continuous Integration, Data Architecture, Information Engineering, Data Security, Cursor, Python (Programming Language), Machine Learning, Language Modeling, Role-Based Access Control, Standard Sql, Search Technologies, Software Engineering, Feature Engineering, Retrieval-Augmented Generation, Large Language Models, Claude Code, Snowflake, Apache Spark, Multi-Cloud, AI Coding Agents, Langfuse — LLM Observability and Analytics Platform, Agentic-AI, Build Management, LangSmith, Machine Learning Operations, Api Design, Terraform, Databricks
- **Published:** October 1, 2026
- **Apply:** https://www.collabera.com/submit-resume/

## About the Role

o 6+ years of data engineering experience with workstream ownership.
o Expert-level Python and SQL skills.
o Experience with Java or Scala as needed by client environments.
o Hands-on experience with Databricks or Snowflake (program requirement).
o Strong expertise in:
# Apache Spark
# Airflow, Dagster, or Kedro
# Lang Chain
o Production GenAI or agentic AI experience:
# RAG
# Embeddings
# Vector search
o Terraform experience.
o Hands-on experience with Multi-Cloud
o Strong API development experience.
Preferred Qualifications
o LLMOps and agent evaluation tools:
o LangSmith, Client-facing consulting experience.
Experience working with senior business stakeholders.

## Description

A hands-on engineering role responsible for building the data foundations that support client's LLM, retrieval, and agentic AI solutions. This is the execution-focused version of the Senior Data Engineer role without workstream leadership responsibilities.
For Role 2: Multiple positions
Own the data engineering workstream supporting McKinsey's GenAI and agentic AI client solutions across industries including government, defense, aerospace, utilities, and oil & gas
o Design and build scalable data architectures and pipelines supporting Machine Learning, GenAI, and agentic AI solutions.
o Deliver end-to-end RAG implementations, including:
# Embeddings
# Vector databases
# Context construction
# Output processing
o Build MLOps and LLMOps platforms:
# CI/CD for data and models
# Model observability
# Automated retraining
# Agent evaluation frameworks
o Implement data security and governance controls:
# PII/PHI protection
# Authentication
# RBAC
o Lead feature engineering initiatives.
o Mentor junior engineers and provide technical guidance.


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