US & HCMC - Senior Software Engineer, Data Infrastructure

TinyFish • Palo Alto, CA

3d • Remote

About The Position

TinyFish is seeking a Senior Software Engineer, Data Infrastructure, to own how data is stored, traced, governed, and accounted for across the company. The role will build infrastructure that product services, pipelines, and analytics depend on, including storage standards, lineage and tracing, data lifecycle controls, customer data commitment enforcement, cost attribution, and shared data definitions.

A customer request can span multiple services, LLM providers, cloud platforms, and browser workloads, producing logs, traces, screenshots, HTML, usage events, and results that land in operational databases, blob storage, S3/Iceberg, Databricks, and product analytics. The engineer will make answers about where data is, what it costs, and how it is handled fast, self-serve, consistent, and provable for engineers and teams across sales, support, security, legal, and finance.

The role owns problems end to end: mapping undocumented data paths, designing and implementing systems, and ensuring they remain correct as services, tables, and stores are added. It will turn existing practices for commitments such as no training on customer data, zero data retention, and GDPR deletion and export into infrastructure that works by default. The work also includes establishing storage guidelines and paved roads, propagating identity, policy, and lineage, providing reliable cost attribution, and enabling non-engineering teams to answer data questions without an engineer in the loop.

The position collaborates closely with product engineers and security, legal, sales, support, and finance. TinyFish describes its working environment as remote, flexible, and async-friendly, and offers broad ownership, challenging problems at the intersection of AI, browser automation, and distributed systems, and a culture emphasizing engineering judgment, simplicity, security, and ownership.

Requirements

• Significant experience designing and building production data infrastructure that other engineers build on, such as storage architecture, tracing or lineage, data access layers, or data platform tooling.
• Strong systems thinking and ability to follow data across application code, service boundaries, queues, storage systems, clouds, and analytics platforms.
• Experience implementing data lifecycle and data-handling requirements, such as retention, deletion, data export, legal hold, consent or opt-out flags, GDPR/CCPA, or zero data retention, and maintaining coverage as systems grow.
• A track record of building controls and standards that other teams adopt, such as defaults, libraries, infrastructure modules, schemas, and automated checks.
• Sound judgment across correctness, cost, latency, and delivery speed.
• Strong software engineering skills and production experience with infrastructure as code.
• Working knowledge of relational stores, object storage, table formats such as Iceberg or Delta, and warehouses or lakehouses, including when each is appropriate.
• Experience using AI coding tools effectively, with the judgment to verify and own their output.
• Clear communication, including the ability to explain data commitments and tradeoffs to engineers, legal teams, and customers.
• An enablement mindset, with success measured partly by how well technical and non-technical teams can get answers independently.

Nice To Haves

• Experience with distributed tracing and context propagation, such as OpenTelemetry.
• Experience with lineage or catalog systems, such as OpenLineage, Unity Catalog, or DataHub.
• Experience with Databricks, Spark, or Iceberg at scale, including deletes, compaction, and snapshot management.
• Experience with cost attribution, chargeback, or FinOps for cloud or LLM spend, especially across multiple clouds or providers.
• Experience with product analytics platforms such as PostHog or Amplitude and maintaining consistent metric and cohort definitions across systems.
• Background in privacy engineering or experience with SOC 2, ISO 27001, or customer data-handling audits.
• Experience with AI/ML data handling, such as training-data preparation, LLM provider data policies, or evaluation datasets.
• AWS experience, including Terraform and multi-account environments.

Responsibilities

• Map how data flows through services, clouds, and analytics systems, including undocumented paths, and identify where it lands.
• Design and build propagation of identity, data policy, and lineage—including tenant, policy flags, and root request ID—across synchronous calls, queues, internal jobs, and data pipelines.
• Enforce data policies, retention, deletion, and legal holds where data is written and stored, ensuring missing context fails safe.
• Build cost attribution and lineage systems that product, finance, and leadership can rely on.
• Develop and evolve data-handling and storage guidelines, and implement them through Terraform modules, libraries, schema conventions, defaults, and CI checks.
• Build guardrails that bring new tables, services, and data stores under policy by default or automatically flag uncovered resources.
• Define shared cohorts and data definitions and make them the default across analytics and data systems.
• Monitor for drift and coverage gaps and drive fixes to root causes.
• Partner with security, legal, and customer-facing teams to translate data commitments and contract terms into engineering requirements.
• Build reports, tools, and evidence that enable sales, support, legal, security, and finance to answer data questions themselves.
• Participate in incident response for data-handling issues and drive systemic corrective actions.
• Establish storage standards and reusable defaults, including plans to migrate fast-growing operational tables to appropriate storage tiers.
• Support end-to-end handling of deletion, export, and no-train requests, including respect for legal holds and provable outcomes.

Benefits

• Broad ownership over how data is stored, governed, and accounted for across TinyFish.
• Close collaboration with product engineers and teams in security, legal, sales, support, and finance.
• Hard problems at the intersection of AI, browser automation, and distributed systems.
• A culture that values engineering judgment, simplicity, security, and ownership.
• A remote, flexible, and async-friendly working environment.
• Competitive compensation.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed