Tech Lead, Data & Inference Engineer

About the job Tech Lead, Data & Inference Engineer Our Client A fast moving and venture backed advertising technology startup based in San Francisco. They have raised twelve million dollars in funding and are transforming how business to business marketers reach their ideal customers. Their identity resolution technology blends business and consumer signals to convert static audience lists into high match and cross channel segments without the use of cookies.

By transforming first party and third party data into precision targetable audiences across platforms such as Meta, Google and YouTube, they enable marketing teams to reach higher match rates, reduce wasted advertising spend and accelerate pipeline growth. With a strong understanding of how business buyers behave in channels that have traditionally been focused on business to consumer activity, they are redefining how business brands scale demand generation and account based efforts. 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.

Location : San Francisco Work type : Full Time, Compensation : above market base bonus equity Roles & Responsibilities 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.