Hybrid Backend Engineer - Data Infrastructure & Quality

Skill; City of Niagara Falls (NY); Hybrid; USD 80,000 - 90,000; Full time

Benefits offered by this job

Health benefit contributions

Retirement plan with match

Flexible spending accounts

Job summary

Skill is seeking a Backend Engineer - Data Infrastructure / Data Quality to build backend services for our data platform. You will design AI-enabled tooling, enhance data quality, and scale systems across the org.

The role emphasizes strong Java/Python skills, production-grade pipelines, and collaboration with data engineers to reduce operational risk. The candidate should thrive in a hybrid NYC environment, working 2–3 days onsite, with a focus on observability, data quality validation, and

Qualifications

• Backend engineer with experience in Platform Engineering or large-scale data pipelines.
• Strong Java or Python background; solid systems design and data structures knowledge.
• Able to work with stakeholders, write clear technical docs, and design stable APIs/contracts.
• Committed to code quality, testing, and reliable software delivery.

Responsibilities

• Build and maintain backend services supporting core data use-cases.
• Design and integrate AI features for data quality, observability, and tooling.
• Develop tooling to promote company-wide data best practices.
• Create infrastructure to democratize data and improve data usage across the company.
• Scale systems collaboratively with teammates and stakeholders.
• Understand user workflows to improve data quality and reliability.

Skills

Python

Java

Data pipelines

Platform engineering

Data quality

Observability

Tools

Kubernetes

GCP

Databricks

Snowflake

Airflow

Flyte

Iceberg

Dataflow

Job description

Skill is seeking a Backend Engineer - Data Infrastructure / Data Quality to build backend services for our data platform. You will design AI-enabled tooling, enhance data quality, and scale systems across the org.

The role emphasizes strong Java/Python skills, production-grade pipelines, and collaboration with data engineers to reduce operational risk. The candidate should thrive in a hybrid NYC environment, working 2–3 days onsite, with a focus on observability, data quality validation, and