Jobs in the United States

25+ jobs

Save this search

Added just now
C
Senior QA Automation Engineer - Playwright & Selenium
Cognizant
Raleigh, NC
- $50,000 to $92,500 USD / year
We are seeking a Senior QA Automation Engineer - Playwright & Selenium to join our Quality Engineering team supporting critical financial applications. The ideal candidate will have strong…
Added just now
NA
Front Office Automation Tester
NTT America, Inc.
Charlotte, NC
- $90,000 to $115,000 USD / year
We are seeking a Senior QA Automation Engineer to support Front Office Sales & Trading technology platforms. This role is responsible for designing, developing, and executing automated…
Added just now
Sr DevOps Engineer
Insight Global
Charlotte, NC
Insight Global is seeking a Sr DevOps Engineer for a large financial services client. This individual will be an experienced hands-on DevOps Engineer with more than seven years of…
Added 4 mins ago
IG
Software Engineer- AI- Poland
Insight Global
Remote, United States
- From $40 USD / hour
Experience developing or improving agentic software-development workflows, repository instructions, automated verification, or developer tooling.
Added 4 mins ago
IG
Senior Test Engineer/ Test Lead
Insight Global
Bothell, WA
- From $40 USD / hour
The Senior Test Engineer will work closely with the system integrator responsible for installing and configuring the solution within the organization's environment, as well as internal…
Added 5 mins ago
K
Associate DevOps Software Engineer
Keenfinity
Fairport, NY
- $80,000 to $87,000 USD / year
Radionix is seeking an Entry-Level DevOps Engineer at our facility located at 130 Perinton Parkway in Fairport, NY. In this role, you will join our engineering team to help build, maintain,…
Added 8 mins ago
Network Engineer III
Chenega Corp
Lawrence, KS
The Network Engineer III provides enterprise network engineering and operational support for DOI/IA network environments, including LAN, WAN, WLAN, VoIP, and security-related…
Added 12 mins ago
Senior Security Software Engineer
Raydar
San Francisco, CA
- Implement scoped permissions and approvals so automated actions never exceed the authority of the user or organization that started them.
Added 12 mins ago
Senior AI Engineer
Raydar
San Francisco, CA
…production use, with ownership at every stage. The role suits an engineer who wants to decide how much independent action an automated system should…
Added 13 mins ago
Project Engineer II
Oceaneering
Panama City, FL
…production documentation, design reports, assembly procedures, test plans, procedures, and final reports,…
Added 21 mins ago
O
Senior Platform Software Engineer - OCI
Oracle
Hartford, CT
- $92,500 to $209,500 USD / year
Oracle Cloud Infrastructure (OCI) is looking for a Senior Platform Software Engineer to join the PKI team responsible for building and operating cloud-scale Public Key Infrastructure…
Added 21 mins ago
Data Scientist 1
Baylor Scott & White Health
Hartford, CT
internal tools that accelerate development and test whether outputs are accurate, useful, and reproducible.
Added 21 mins ago
C
Senior QA Automation Engineer - Playwright & Selenium
Cognizant
Hartford, CT
- $50,000 to $92,500 USD / year
We are seeking a Senior QA Automation Engineer - Playwright & Selenium to join our Quality Engineering team supporting critical financial applications. The ideal candidate will have strong…
Added 23 mins ago
Test Engineer - Electrical
Enovation Controls LLC
OK
The Test Engineer – Electrical, within the Engineering team, is responsible for developing, configuring, and executing electrical design verification (DV) and production validation (PV)…
Added 24 mins ago
CR
Full Stack Software Engineer - Technical Lead, AVP II
Charles River Systems Inc MA
United States
- $90,000 to $157,500 USD / year
Experience with CI/CD and modern DevOps practices, including automated build, test, and deployment pipelines.
Added 28 mins ago
Lead Full-Stack Developer
LMI
United States
Design, build, review, test, and maintain full-stack features across responsive web interfaces, backend services, application programming interfaces (APIs), data stores, and cloud…
Added 29 mins ago
A
Integration And Test Engineer
Actalent
Syracuse, NY
- $92.5 to $112.85 USD / hour
Integration & Test Engineer Integration & Test Engineer will develop and execute advanced testing strategies for software and system-level applications that support electronic warfare,…
Added 30 mins ago
UG
Lead AI/ML Platform Engineer - Remote
UnitedHealth Group
Eden Prairie, MN
- $145,500 to $249,500 USD / year
…systems across the business, spanning intake and registration, automated governance gating, request routing, and runtime observability and monitoring. Today, these capabilities operate as…
Added 30 mins ago
Java Test Engineer
Infosys
United States
Quality Engineering leadership, Java, Java Microservices testing, REST API testing, REST Assured, Selenium, automated test framework development, functional and integration testing,…
Added 32 mins ago
SC
Aviation Software Engineer
SITEC Consulting
Lexington Park, MD
- $100,000 to $135,000 USD / year
Software Testing & Validation: Evaluate software testing results, automated test processes, code coverage, and verification activities to ensure system readiness.
Added 34 mins ago
Cloud Migration Developer
AGFA HEALTHCARE CORP
MILWAUKEE, WI
Added 34 mins ago
Cyber Security Engineer
mthree
Alpharetta, GA
We are looking for a highly motivated Cyber Security Engineer to join a dynamic team with one of one our clients. We're seeking someone to join Customer Authentication team as a Software…
Added 37 mins ago
IG
Remote QA Engineer
Insight Global
Remote, United States
- From $68 USD / hour
A large bank/ financial company in the Cincinnati, OH area is seeking a QA Engineer/ Tester to help with manual and automated UI testing. This is a contract opportunity through the end of…
Added 38 mins ago
Automation Engineer
TE Connectivity
#, NC
TE Connectivity's Manufacturing & Process Development Engineering team is seeking an Automation Engineer to join the Central Assembly & Automation team, supporting the identification,…
Added 38 mins ago
Digital Engineer
TE Connectivity
#, NC
…that improve engineering and manufacturing productivity. The engineer will link systems with machines and manufacturing processes – connecting equipment, test and inspection systems, and…

TinyFish

US & HCMC - Senior Software Engineer, Data Infrastructure

Palo Alto, CA, United States

Apply About employer

Applications open in a new tab.

Job details

Posting language: English

Original job description

About TinyFish TinyFish builds browser-based AI agents that do real work on the open web for enterprise customers. We're a small, technical team shipping fast against serious enterprise buyers, and they ask serious questions about where their data goes. About the Role We're looking for a Senior Software Engineer, Data Infrastructure, to own how data is stored, traced, governed, and accounted for across TinyFish. The goal is to make the answers to "where is this data, what did it cost, and is it handled the way we promised?" fast, consistent, and provable. This is not a pipeline-maintenance or data-request role. You'll build the infrastructure that our product services, pipelines, and analytics sit on:

storage standards and the paved road for using them;
lineage and tracing across services and clouds;
data lifecycle controls such as retention, deletion, export, and legal hold;
enforcement of customer data commitments;
cost attribution;
shared definitions that keep our analyses consistent.

A single customer request at TinyFish fans out across multiple services, LLM providers, cloud platforms, and browser workloads. Along the way it produces logs, traces, screenshots, HTML, usage events, and results. Those land in operational databases, blob storage, S3/Iceberg, Databricks, and product analytics. We can answer most questions about that data today, but too often it takes an engineer digging through logs, code, and dashboards, and different systems give slightly different answers. You'll make those answers fast, self-serve, and consistent, for engineers and for the sales, support, security, legal, and finance teams who need them. You'll own problems end to end: finding the paths nobody has documented, designing the system, implementing it, and making sure it stays correct as engineers add new services, tables, and stores. We care more about your engineering judgment and the coverage you can prove than about any particular tool. Why this role, why now We already handle commitments like "don't train on my data," zero data retention (ZDR), and GDPR deletion and export requests, and we can attribute AI and infrastructure costs to the tenants and features that caused them. Today, though, much of that depends on careful per-service engineering and manual investigation. At the same time, some of our operational stores are growing faster than they should, because there isn't yet a clear standard for where different kinds of data belong. Our internal analyses also rely on hand-written filters that aren't consistent from one system to the next. All of these costs grow with every new service, data store, and enterprise contract. These problems share a foundation: identity, policy, and lineage that travel with the data, clear standards for where data lives, and shared definitions everyone uses. You'll turn what we do carefully today into infrastructure that does it by default. Then the answers can reach the people who need them without an engineer in the loop: sales responding to a security review or contract requirement, support handling a customer data request, finance asking what a feature costs. Example projects

Data lifecycle and data subject rights: retention and deletion that propagate to every copy of the data, including derived tables, caches, snapshots, and third parties. "Download my data" exports that are complete and repeatable. GDPR requests handled by the system rather than by an engineer.
Legal hold: the ability to put constraints on data that must not be deleted, and have deletion and retention respect them everywhere.
Customer data commitments: move "don't train on my data" and ZDR from careful per-service work to platform defaults that cover every path a request can take, including async queues, retries, error paths, and direct internal calls.
Storage standards and paved road: official guidelines for where each kind of data belongs (hot operational stores, large blobs, cold S3/Iceberg, Databricks for large analytics and historical analysis), with retention, partitioning, and cost defaults. Delivered as reusable Terraform modules and libraries so the right choice is the easy one. Includes migrating fast-growing operational tables to the right tier.
Cost lineage: automatic attribution of cost across clouds and providers to the originating tenant and feature. A request that goes A → B → C should reconcile with a direct call to C and with each provider's bill.
Codified cohorts and filters: a single, versioned definition of segments such as internal users, test accounts, employees, and enterprise customers, used consistently across product analytics, Databricks, and our other data systems in place of ad hoc user-ID lists and email regexes.
Self-serve enablement: give sales, support, legal, and finance direct access to provable answers, such as what we can commit to in an enterprise contract, the status of a customer's deletion or export request, or cost by tenant, without pulling in an engineer.

What You'll Do

Map how data actually flows through our services, clouds, and analytics systems, and where it lands, including the paths that aren't on any diagram.
Design and build propagation of identity, data policy, and lineage (tenant, policy flags, root request ID) across sync calls, queues, internal jobs, and data pipelines.
Enforce data policies, retention, deletion, and legal holds at the points where data is written and stored, so that missing context fails safe.
Build cost attribution and lineage systems that product, finance, and leadership can rely on.
Write and evolve data handling and storage guidelines, then turn them into Terraform modules, libraries, schema conventions, defaults, and CI checks so engineers rarely need to read them.
Build guardrails so new tables, services, and data stores are automatically brought under policy, or at least automatically flagged, instead of depending on someone remembering.
Define shared cohorts and data definitions, and make them the default across our analytics and data systems.
Monitor for drift and coverage gaps, and drive fixes to the root cause rather than one-off patches.
Partner with security, legal, and customer-facing teams to translate data commitments and contract terms into engineering requirements.
Treat sales, support, legal, security, and finance as customers of your systems. Build the reports, tools, and evidence they need to answer data questions themselves.
Participate in incident response for data-handling issues, and drive the systemic corrective actions that follow.

What success looks like

30 days. You've mapped where customer data and cost flow across our core services and data systems, including the undocumented paths. You know which parts of our data lifecycle, commitment, and attribution coverage are automated and which depend on manual work. You know which questions from sales, support, legal, and finance cost the most engineering time to answer.
60 days. Storage guidelines v1 are published, with a paved road for the most common cases, and the fastest-growing operational tables have a plan. Propagated request context runs across our core services. A first cost attribution view by tenant and feature exists, and the team uses it. Core cohorts, such as internal and test users, have one shared definition.
90 days and beyond.
ZDR, no-train, and retention are enforced where data lands, with automated checks that catch new tables and stores that aren't covered.
New services and data stores come under policy by default.
Deletion, export, and no-train requests run through the system, respect legal holds, and can be proven end to end.
Lineage can answer "where is tenant X's data?"
Cost attribution reconciles with provider bills across clouds, and finance gets cost by tenant and feature without asking engineering.
Sales can see what we can commit to in a contract, and support answers customer data requests from the system.
Internal analyses use shared cohorts everywhere.

What We're Looking For

Significant experience designing and building production data infrastructure that other engineers build on: storage architecture, tracing or lineage, data access layers, or data platform tooling.
Strong systems thinking. You can 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. You've also made that coverage hold up as the system grew.
A track record of building controls and standards other teams actually adopt: defaults, libraries, infrastructure modules, schemas, and automated checks rather than wiki pages.
Sound judgment across correctness, cost, latency, and delivery speed.
Strong software engineering skills, and production experience with infrastructure as code.
Working knowledge of the storage landscape (relational stores, object storage, table formats like Iceberg or Delta, warehouses or lakehouses) and when each is the right fit.
Experience using AI coding tools effectively, along with the judgment to verify and own what they produce.
Clear communication, including explaining data commitments and tradeoffs to engineers, legal, and customers.
An enablement mindset: you measure success partly by how well other teams, technical and non-technical, can get answers without you.

Nice to Have

Experience with distributed tracing and context propagation (e.g. OpenTelemetry).
Experience with lineage or catalog systems (e.g. OpenLineage, Unity Catalog, 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 (e.g. PostHog, Amplitude) and keeping metric and cohort definitions consistent across systems.
Background in privacy engineering, or experience with SOC 2, ISO 27001, or customer data-handling audits.
Experience with AI/ML data handling: training-data preparation, LLM provider data policies, or eval datasets.
AWS experience, including Terraform and multi-account environments. What We Offer
Broad ownership over how data is stored, governed, and accounted for across TinyFish.
Close collaboration with the engineers building our products, and with 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.

Apply About employer

More about this job

Skills

Direct inward dialing
HTML
Operational databases
Databricks
Sales
IBM Terraform
Leadership
Incident response
Systems thinking
Wiki
Infrastructure as code
Communication skills
Unity
Apache Spark
ML
AWS

Report this job