Senior Data Engineer

GENNTE Technologies; Pittsburgh (Allegheny County); Hybrid; USD 100,000 - 140,000; Full time

Job summary

GENNTE Technologies in Pittsburgh is seeking an experienced Lead Data Engineer to drive the design and delivery of scalable data platforms and pipelines. This role demands strong expertise in Python, SQL, and modern data engineering practices, with a focus on leading teams and implementing DevOps-driven workflows.

The ideal candidate will manage scalable ETL/ELT pipeline design, define best practices, and enhance the performance and reliability of data architectures. Strong problem-solving skills and an ownership mindset are essential.

Qualifications

• Strong hands-on experience in Python and SQL.
• Deep understanding of data engineering concepts (ETL/ELT, data modeling).
• Proven experience in designing production-grade data pipelines.
• Strong exposure to DevOps practices and tools.

Responsibilities

• Lead the design and implementation of scalable ETL/ELT pipelines.
• Define best practices for data engineering.
• Own the end-to-end delivery of data pipelines.
• Drive the implementation of CI/CD in workflows.
• Mentor and guide junior engineers.

Skills

Python

SQL

DevOps practices

ETL/ELT

Data modeling

Tools

Jenkins

Docker

Airflow

Job description

We are looking for an experienced Lead Data Engineer to drive the design and delivery of scalable data platforms and pipelines. This role requires strong expertise in Python, SQL, and modern data engineering practices, along with the ability to lead teams, define standards, and implement DevOps-driven data workflows.

Key Responsibilities:

Lead the design and implementation of scalable ETL/ELT pipelines and data architectures.

Define best practices and standards for data engineering, code quality, and performance optimization.

Own end-to-end delivery of data pipelines, ensuring reliability, scalability, and maintainability.

Build and optimize frameworks for data ingestion, transformation, and processing of large datasets.

Drive implementation of CI/CD and DevOps practices across data engineering workflows.

Oversee automation for data pipeline deployment, monitoring, and alerting.

Collaborate with business and technology stakeholders to translate requirements into scalable solutions.

Mentor and guide junior engineers; conduct code reviews and provide technical leadership.

Lead troubleshooting, root cause analysis, and performance tuning across data platforms.

Must-Have Skills:

Strong hands-on experience in Python and SQL with the ability to guide others.

Deep understanding of data engineering concepts (ETL/ELT, data modeling, warehousing).

Proven experience designing and leading production-grade data pipelines and platforms.

Strong exposure to DevOps practices and tools (Git, CI/CD pipelines, Jenkins, Docker).

Experience with system design, scalability, and performance optimization.

Strong problem-solving skills with ownership mindset.

Good to Have:

Experience with PySpark or large-scale data processing frameworks.

Exposure to cloud data platforms (AWS, Azure, or GCP).

Experience with workflow orchestration tools (e.g., Airflow).

Understanding of data governance, data quality, and lineage frameworks.