Tech Lead, Data & Inference Engineer

Tech Lead, Data & Inference Engineer

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 minute‑level latency and optimizing cost per terabyte. Conduct root‑cause analysis and provide long‑lasting solutions.

Operate inference pipelines that enhance and enrich data—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 APIs 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.

Work type:
Full Time.

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).

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.