Tech Lead - Data Platform & Inference Engineering

Catalyst Labs; New York (NY); Hybrid; USD 120,000 - 160,000; Full time

Job summary

A leading tech company in New York City is seeking a Tech Lead, Data & Inference Engineer to lead the design and development of a robust data platform. This role involves scaling data pipelines, ensuring reliability and data quality, and mentoring engineers across teams. Candidates should have 6 to 12 years of experience in building production-grade data systems, proficiency in SQL and Python, and familiarity with distributed data technologies. The position supports hybrid or distributed work environments.

Qualifications

• 6 to 12 years of experience building and scaling production-grade data systems.
• Deep expertise in data architecture, modeling, and pipeline design.
• Excellent written and verbal communication; proactive and collaborative mindset.

Responsibilities

• Lead the design, development and scaling of an end-to-end data platform.
• Build and maintain scalable batch and streaming pipelines.
• Take full ownership of reliability, cost, and service-level objectives.

Skills

SQL (query optimization on large datasets)

Python

Data architecture

Data pipeline design

Distributed data technologies (Spark, Flink, Kafka)

Modern orchestration tools (Airflow, Dagster, Prefect)

Node.js

Education

Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or Mathematics

Tools

Kubernetes

Cloud infrastructure (AWS, GCP, or Azure)

dbt

DuckDB

IaC

CI/CD

Job description

A leading tech company in New York City is seeking a Tech Lead, Data & Inference Engineer to lead the design and development of a robust data platform. This role involves scaling data pipelines, ensuring reliability and data quality, and mentoring engineers across teams. Candidates should have 6 to 12 years of experience in building production-grade data systems, proficiency in SQL and Python, and familiarity with distributed data technologies. The position supports hybrid or distributed work environments.