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 deeply embedded in our clients' recruitment operations. We collaborate directly with founders, CTOs, and heads of AI 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.

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 deeply embedded in our clients' recruitment operations. We collaborate directly with founders, CTOs, and heads of AI 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.

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 APIs into trusted and low-latency systems.
• Take full ownership of reliability, cost, and service level objectives, including achieving 99.9% 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, including 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, documenting 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

• Bachelor's or Master's 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 action to identify bottlenecks, automate workflows, and iterate rapidly based on measurable outcomes.
• Demonstrated ability to teach, mentor, and document technical decisions and schemas clearly.

• 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).
• Strong Node.js skills for faster onboarding and system integration (bonus).

Core Experience

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