Tech Lead, Staff Software Engineer, Data Product (US)

Role overview We are hiring a Staff Software Engineer / Tech Lead to own mission-critical data services on a Data Product team that turns blockchain transaction activity into investigative intelligence at petabyte scale. The work spans designing intricate data models, building highly scalable features that integrate with dozens of blockchains, and partnering with data scientists, backend engineers, and product managers to deliver sub-second-latency insights. This is a hands-on technical leadership role for someone who writes clear design docs, communicates tradeoffs, and builds alignment across disciplines.

Responsibilities - Architect and build scalable data services and APIs that power analytics over multi-blockchain data at petabyte scale. - Design data models optimized for storage and retrieval, targeting sub-second query latency for end-user experiences. - Lead 0-to-1 initiatives: build pipelines, data platforms, or ML/BI workflows from scratch rather than maintaining legacy systems. - Collaborate with data scientists, backend engineers, product managers, and customer-facing teams to translate user needs into scalable data solutions. - Mentor engineers and analysts, reviewing designs and leveling up the broader team. - Champion cost-conscious design for performance, scale, and efficiency across the data stack.

Requirements - Bachelor's degree (or equivalent) in Computer Science or a related field. - 5+ years of hands-on experience architecting scalable API development and distributed systems, with a track record of taking projects from ideation to production deployment. - Strong programming skills in Python plus SQL or SparkSQL. - In-depth experience with data stores such as BigQuery and Postgres. - Proficiency with pipeline and workflow orchestration tools like Airflow and DBT. - Experience with data processing and streaming technologies including Dataflow, Spark, Kafka, and Flink, plus deploying and monitoring infrastructure on public clouds using Docker, Terraform, Kubernetes, and Datadog.

Nice to have - Experience with LLMs or AI-powered workflows, such as prompt engineering, internal tooling, or semantic search.

Benefits and work setup - Distributed team across multiple countries and time zones, with a culture grounded in trust, transparency, and adaptability. - Open communication and regular check-ins to keep collaboration tight despite geographic spread.