Data Engineer

Key Responsibilities

Design, build, and maintain robust ETL/ELT pipelines and data workflows using Python.
Implement best practices for data architecture, modeling, and processing at scale.
Collaborate with team members to leverage Azure DevOps for CI/CD, release management, and automated testing.
Ensure compliance with data governance, quality, and security standards.
Mentor junior engineers and contribute to the growth of engineering best practices.
Partner with stakeholders (data scientists, analysts, business teams) to deliver reliable data solutions.

Qualifications

5+ years of professional experience in data engineering or related roles.
Strong proficiency in Python, with proven experience in data-focused libraries (e.g., PySpark, Pandas).
Hands-on experience designing and consuming RESTful APIs or GraphQL endpoints.
Proficiency in SQL and relational databases (e.g., SQL Server, PostgreSQL, MySQL).
Solid experience with data lakes and data warehouses.
Familiarity with cloud environments is a plus, with Azure experience preferred.
Familiarity with Microsoft Fabric (Data Lakehouse, Fabric pipelines, or Fabric Data Warehouses) is a strong plus.
Hands-on experience with Azure DevOps tools: pipelines, repositories, boards, and artifacts.
Strong knowledge of CI/CD pipelines and version control (Git).
Excellent problem-solving, communication, and collaboration skills.

Preferred Skills

Exposure to machine learning workflows and MLOps practices.
Experience using donor management systems (Salesforce NPSP in particular).
Knowledge of containerization a plus (Docker/Kubernetes).
Some familiarity with Infrastructure as Code (IaC) tools such as Terraform, Bicep, or ARM templates is a bonus.