Tech Lead, Data & Inference Engineer

Catalyst Labs; North Carolina; Hybrid; USD 120,000 - 160,000; Full time

Job summary

A leading talent agency is looking for a Tech Lead, Data & Inference Engineer to oversee the design and scaling of their data platform. The ideal candidate will have 6 to 12 years of experience in building production-grade data systems with expertise in SQL, Python, and various data technologies. This role involves mentoring engineers, ensuring data consistency, and promoting best practices. It requires excellent communication skills and a proactive mindset. Join a collaborative team focused on driving applied AI solutions.

Qualifications

• 6 to 12 years of experience building and scaling data systems.
• Demonstrated ability to teach and document technical decisions.
• Familiarity with dbt and modern data stack.

Responsibilities

• Lead the design and development of a data platform.
• Build and maintain batch and streaming data pipelines.
• Mentor engineers and promote data-driven practices.

Skills

Expert SQL

Python

Data architecture expertise

Mentoring and teaching ability

Proactive mindset

Education

Bachelor's or Master's in Computer Science

Tools

Spark

Flink

Kafka

Airflow

Kubernetes

AWS

Job description

North Carolina, North Carolina, United States

About the job Tech Lead, Data & Inference Engineer

Our Client

About Us

Catalyst Labs is a leading talent agency with a specialized vertical in Applied AI, Machine Learning, and Data Science. We stand out as an agency thats deeply embedded in our clients recruitment operations.

We collaborate directly with Founders, CTOs, and Heads of AI in those themes who are driving the next wave of applied intelligence from model optimization to productized AI workflows. We take pride in facilitating conversations that align with your technical expertise, creative problem-solving mindset, and long-term growth trajectory in the evolving world of intelligent systems.

Work type: Full Time,

Lead the design, development and scaling of an end to end data platform from ingestion to insights, ensuring that data is fast, reliable and ready for business use.

Build and maintain scalable batch and streaming pipelines, transforming diverse data sources and third party application programming interfaces into trusted and low latency systems.

Take full ownership of reliability, cost and service level objectives. This includes achieving ninety nine point nine percent uptime, maintaining minutes level latency and optimizing cost per terabyte. Conduct root cause analysis and provide long lasting solutions.

Operate inference pipelines that enhance and enrich data. This includes enrichment, scoring and quality assurance using large language models and retrieval augmented generation. Manage version control, caching and evaluation loops.

Work across teams to deliver data as a product through the creation of clear data contracts, ownership models, lifecycle processes and usage based decision making.

Guide architectural decisions across the data lake and the entire pipeline stack. Document lineage, trade offs and reversibility while making practical decisions on whether to build internally or buy externally.

Scale integration with application programming interfaces and internal services while ensuring data consistency, high data quality and support for both real time and batch oriented use cases.

Mentor engineers, review code and raise the overall technical standard across teams. Promote data driven best practices throughout the organization.

Qualifications

Bachelors or Masters degree in Computer Science, Computer Engineering, Electrical Engineering, or Mathematics.

Excellent written and verbal communication; proactive and collaborative mindset.

Comfortable in hybrid or distributed environments with strong ownership and accountability.

A founder-level bias for actionable to identify bottlenecks, automate workflows, and iterate rapidly based on measurable outcomes.

Demonstrated ability to teach, mentor, and document technical decisions and schemas clearly.

Core Experience

6 to 12 years of experience building and scaling production-grade data systems, with deep expertise in data architecture, modeling, and pipeline design.

Expert SQL (query optimization on large datasets) and Python skills.

Hands-on experience with distributed data technologies (Spark, Flink, Kafka) and modern orchestration tools (Airflow, Dagster, Prefect).

Familiarity with dbt, DuckDB, and the modern data stack; experience with IaC, CI/CD, and observability.

Exposure to Kubernetes and cloud infrastructure (AWS, GCP, or Azure).

Bonus: Strong Node.js skills for faster onboarding and system integration.

Previous experience at a high-growth startup (10 to 200 people) or early-stage environment with a strong product mindset.