Senior Data Engineer: Driving Data Pipelines & Insights

The Home Depot; Atlanta (GA); On-site; USD 120,000 - 180,000; Full time

Job summary

The Home Depot is seeking an Online Data Engineer to translate business requirements into data infrastructure, build, test, and maintain data pipelines and APIs for enterprise data use.

Responsibilities include collaboration with internal teams, project leadership, and developing scalable analytics solutions using Python, SQL, PySpark, Airflow, and BigQuery. The role emphasizes data quality, automation, and continuous improvement.

Qualifications

• Bachelor’s degree or equivalent in a quantitative field.
• 4+ years of relevant work experience.
• Experience in predictive modeling, data mining and data analysis.
• Experience developing and testing ETL jobs/pipelines, configuring orchestration, automated CI/CD, writing automation scripts, and supporting the pipelines in production.
• Experience in high-level programming languages such as Python.
• Experience defining and capturing metadata and rules associated with ETL processes.

Responsibilities

• Collaborate with business partners and cross-functional teams to translate requirements into analytics solutions; drive data-led decisions.
• Lead project goals, prioritize work, and provide mentoring to junior engineers.
• Communicate insights and recommendations to technical and non-technical leaders; prepare progress reports and presentations.
• Guide requirements gathering and design for complex applications and data pipelines.

Skills

Predictive modeling

Data mining

ETL pipelines

CI/CD automation

Python

SQL queries

PySpark

Airflow

DataProc

React

JavaScript

Google BigQuery

Education

Bachelor’s degree in a quantitative field

Master’s degree

Tools

Google BigQuery

PySpark

Airflow

DataProc

React

Nucleus

Job description

The Home Depot is seeking an Online Data Engineer to translate business requirements into data infrastructure, build, test, and maintain data pipelines and APIs for enterprise data use.

Responsibilities include collaboration with internal teams, project leadership, and developing scalable analytics solutions using Python, SQL, PySpark, Airflow, and BigQuery. The role emphasizes data quality, automation, and continuous improvement.