Airbyte
Data integration platform with connectors catalog
Customer Support Developer - Databases
Remote within U.S. Pacific, Mountain, or Central time zones.
About the job
- At least 2 years of experience in software engineering, data engineering, technical support, DevOps, quality assurance, or a similar role supporting developers or technical users.
- Fluency in SQL, with hands-on experience investigating query plans, isolation levels, and performance bottlenecks.
- Proficiency in Java or Kotlin, including the ability to read, write, and debug Java Virtual Machine-based connector code.
- Hands-on experience with relational databases, data warehouses, or data lakes, including change data capture, schema evolution, and query optimization.
- Experience with Java Database Connectivity and common database drivers, including debugging connection, authentication, Secure Sockets Layer, and Secure Shell tunnel issues.
- Understanding of cloud platforms such as Amazon Web Services, Google Cloud Platform, or Azure and data integration concepts, including ELT and ETL workflows.
- Ability to troubleshoot issues and communicate clearly with technical and non-technical stakeholders.
- High tolerance for ambiguity and ability to work independently.
- Serve as the primary technical escalation point for database-related customer issues through email, ticketing systems, and video calls.
- Triage and prioritize incoming issues while meeting service-level agreement targets and using artificial intelligence tools to improve resolution speed.
- Investigate complex issues across relational databases, data warehouses, data lakes, and cloud object storage, partnering with Engineering to identify root causes and implement fixes.
- Reproduce customer-reported bugs by inspecting connector logs, query plans, replication slots, transaction logs, synchronization state, and destination table state, and document findings for Engineering.
- Support customers with onboarding and troubleshooting for Secure Shell tunnels, network configuration, database user permissions, change data capture setup using write-ahead logs, binary logs, or redo logs, and warehouse or lake authentication.
- Help customers build and debug integrations using Airbyte database sources and destinations, including authentication, Secure Sockets Layer and Secure Shell setup, change data capture, schema evolution, incremental cursors, and typed destination tables.
- Validate database version compatibility across supported engines and releases, maintain testing environments, and reproduce customer-reported behavior on database and warehouse instances.
- Support customers writing to data warehouses including Snowflake, BigQuery, and Redshift; data lakes and object storage including S3, GCS, Azure Blob, Databricks, and Iceberg; and databases including Postgres, MySQL, MSSQL, and ClickHouse.
- Help customers troubleshoot PostgreSQL, MySQL, MSSQL, Oracle, and MongoDB source replication behavior, cursor selection, full-refresh versus incremental versus change data capture trade-offs, and synchronization state recovery.
- Contribute to Airbyte's open-source database connectors and Java/Kotlin Bulk CDK by fixing bugs, improving error handling, hardening schema-evolution and change data capture paths, and submitting pull requests to the Airbyte repository.
- Review and test community contributions to database sources and destinations.
- Maintain knowledge of Airbyte's connector catalog, database and destination releases, and platform updates.
- Represent the customer voice within Airbyte by working with Engineering and Product to surface issues, advocate for feature requests, and influence the database reliability and performance roadmap.
- Partner with Solutions Engineering and Sales Engineering on technical onboarding for enterprise customers, including change data capture architecture reviews, destination schema modeling, and production readiness assessments.
- Train and mentor Technical Support Engineers on database connector concepts, including Java Database Connectivity fundamentals, change data capture patterns, schema evolution, query optimization, destination typing and normalization, and the Airbyte protocol.
- Partner with Sales and Customer Success on technical conversations, onboarding, and expansion efforts.
- Create and maintain internal and external knowledge base articles, troubleshooting guides, and database connector documentation.
- Document research and troubleshooting processes, including change data capture debugging, destination schema reconciliation, and performance-tuning runbooks.
- Identify artificial-intelligence-driven opportunities for automation and process improvement.
- Develop playbooks and skills that encode repeatable troubleshooting procedures, connector debugging patterns, and onboarding runbooks.
- Lead initiatives to address organizational or support gaps and own unique projects.
- Experience with PostgreSQL, MySQL, MSSQL, Oracle, MongoDB, Snowflake, BigQuery, Redshift, ClickHouse, or similar relational and non-relational systems.
- Experience with data lakes and open table formats such as Iceberg and Delta Lake, and cloud object storage such as S3, GCS, and Azure Blob.
- Familiarity with Airbyte's Bulk CDK or contributions to open-source database connectors.
- Experience with container orchestration tools like Kubernetes or Docker and multi-tenant architectures.
- Background supporting developer-facing or embedded/platform products in a B2B SaaS context.
- Experience with CI/CD pipelines and GitHub-based open-source workflows.
- Experience with AI-assisted development tools and building automated playbooks or runbooks.
About the company
Airbyte provides a data integration platform with a large catalog of connectors that lets users move and replicate data across systems. It offers self-hosted or cloud-hosted deployments and a freemium model, with paid enterprise features and support. Its primary product is the extensive connectors catalog that users can build or edit in minutes, saving time on data infrastructure. What sets Airbyte apart is its focus on stability, reliability, and ease of use, along with the flexibility of self-hosted and cloud options and a broad ecosystem of connectors. The goal is to help businesses manage data pipelines efficiently, enabling teams to spend more time creating value for users rather than maintaining data infrastructure.
Company Size
51-200
Company Stage
Series B
Total Funding
$181.3M
Headquarters
San Francisco, California
Founded
2020
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Simplify's Take
What believers are saying
- Airbyte launched semantic search and fine-grained governance on August 25, 2026.
- August 18, 2026 migration preserved pipeline state, easing open-source customers into Airbyte Cloud.
- The platform claims 7,000 enterprises and a $100 million Series C in 2026.
What critics are saying
- Fivetran, Salesforce, ServiceNow, and Zapier are attacking Airbyte’s connector and agent markets now.
- Revelio Labs shows headcount fell from 138 in 2023 to 130 by March 2026.
- If Agentic AI demand stalls, Airbyte’s expensive product pivot leaves open-source monetization exposed.
What makes Airbyte unique
- Airbyte’s open-source Core and cloud share 600-plus connectors across warehouses, apps, and APIs.
- Airbyte Agents’ Context Store pre-indexes enterprise data, reducing runtime API calls and token spend.
- Workspace-level governance separates users, connectors, and agents for enterprise compliance.
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Benefits
Fully remote flexible work environment
Unlimited PTO
Co-working space
Parental leave
Sponsored travel
Open book policy
Continuous learning/training policy
Medical, dental, & vision coverage
Healthcare insurance stipend
Mental health support
401k
FSA
Work Visas
Growth & Insights and Company News
6 month growth
↓ -3%1 year growth
↑ 1%2 year growth
↑ 0%Data Lakes Market: Airbyte launches AI governance tools. Published: August 26, 2026 Airbyte expands agentic data platform with Semantic Search and fine-grained governance. SAN FRANCISCO, United States August 25, 2026 Airbyte, creator of the open data movement platform, has announced major enhancements to its Airbyte Agents platform, introducing semantic search capabilities and fine-grained entity governance policies designed to give AI agents richer, more secure access to enterprise knowledge including data stored across Data Lakes CRMs, SaaS applications, and collaborative tools. The development marks a significant step in addressing two persistent challenges enterprises face as they scale AI from experimentation to production: contextual data access and governance. The new capabilities are built directly into Airbyte's Context Store a replicated, search-optimized index that forms the core of the Airbyte Agents platform. Semantic search now enables AI agents to retrieve information based on meaning rather than exact keyword matches, covering content in Google Drive, Gong call transcripts, Granola meeting notes, and Linear issues. Internal benchmarks cited by the company indicate up to 80% fewer tokens when querying Gong and up to 75% fewer tokens for Linear, compared to native API approaches, translating into lower inference costs and faster response times. Complementing the semantic retrieval layer, Airbyte has introduced entity policies for workspaces a governance framework that allows organizations to define precise access controls at the data connector and data source level. These policies enable enterprises to restrict agent access to specific data sources, separate development and production environments, and align AI access with existing organizational security and compliance requirements. The governance layer is designed to allow enterprises to deploy AI agents at scale without creating duplicate permission models or sacrificing operational control. "AI agents are only as valuable as the context they can safely access," said Michel Tricot, CEO and co-founder of Airbyte. "Organizations don't need another disconnected vector database or another permission system they need agents that understand the information that already exists across their business while respecting the same governance policies employees rely on every day." Key highlights: * Semantic Search Across Enterprise Data: Airbyte Agents now supports meaning-based retrieval across Google Drive, Gong, Granola, and Linear, enabling AI agents to surface relevant information even when exact query terms are absent from source documents. * Fine-Grained Entity Policies: New workspace-level governance controls allow organizations to assign read and write policies to every user and agent across every data connector, ensuring sensitive data remains restricted to authorized parties. * Significant Token Efficiency Gains: The platform's pre-indexed Context Store delivers up to 80% fewer tokens for Gong queries and up to 75% fewer tokens for Linear, reducing inference costs and accelerating agent response times. * Data Lake Integration: AI agents operating on the Airbyte platform can now assemble context on demand from data lakes alongside CRMs, SaaS applications, and wikis with governance enforced uniformly across all sources. Analyst insight: According to analysts at Next Move Strategy Consulting, the global Data Lakes Market was valued at USD 6.82 billion in 2021 and is predicted to reach USD 32.96 billion by 2030, reflecting sustained enterprise demand for scalable, centralized data storage and analytics infrastructure. NMSC analysts note that as AI agent adoption accelerates across industries, the ability to govern and semantically query data lakes in real time is becoming a critical differentiator for enterprise data platforms. Developments such as Airbyte's latest enhancements signal a broader market shift toward context-aware, governance-first data infrastructure a trend that is expected to drive increased investment in data lake modernization and agentic data tooling through the remainder of the decade. Industry outlook: The integration of semantic search and fine-grained governance into enterprise data platforms reflects a maturing phase of AI adoption, where organizations are moving beyond proof-of-concept deployments toward production-grade, compliance-ready AI systems. As data lakes continue to serve as foundational repositories for enterprise analytics and AI workloads, the demand for platforms that can bridge unstructured knowledge with governed, agent-accessible context is expected to intensify. Airbyte's enhancements position the data movement and integration segment as a critical enabler of the broader agentic AI ecosystem, with implications for data governance, cloud infrastructure, and enterprise AI strategy across sectors including financial services, healthcare, and professional services. Prepared By: Sanyukta Deb Sanyukta Deb is a senior content writer and content analyst with expertise in content strategy, audience engagement, and research-driven storytelling. With a strong leadership approach and strategic mindset, she drives content initiatives that strengthen brand communication and audience connection. She combines creativity with analytical insight to develop impactful, value-led content while mentoring collaborative efforts across teams to ensure consistent, meaningful engagement and long-term brand growth across digital platforms. About the reviewer. Debashree Dey is a senior content writer and communications specialist known for crafting audience-focused narratives and insight-driven content strategies. As a published manuscript author, she combines creative storytelling with strategic thinking to strengthen brand messaging, enhance visibility, and drive meaningful audience engagement across digital platforms. With a collaborative leadership approach, she contributes to high-impact communication initiatives that ensure consistency, clarity, and long-term brand value. Outside of work, she finds inspiration in creative projects, design exploration, and storytelling-driven ideas. Share with peers. Its clients. ams OSRAM AR chip milestone boosts smart glasses market. Egypt data center colocation market to reach USD 358.6 mn by 2035. Data center financing faces new risk from community opposition.
Airbyte has expanded its Agentic Data Platform with semantic search and fine-grained governance features. The platform now supports semantic search across content stored in Google Drive, Gong call transcripts, Granola meeting notes, and Linear issues, enabling AI agents to retrieve information based on meaning rather than exact keywords. The company has also introduced entity policies for workspaces, providing precise control over which data connectors and sources can be accessed by users and AI agents. These policies allow organisations to restrict agent access to data sources, separate development and production environments, and align AI access with security requirements. Airbyte reports that semantic search delivers up to 80% fewer tokens when querying Gong and up to 75% fewer tokens for Linear compared to native API approaches. The platform is built on Airbyte's open-source data replication platform and is trusted by 7,000 enterprises.
Airbyte preserves pipeline state in open-source-to-cloud migration. August 18, 2026 Airbyte has launched a migration service that lets organisations move from its self-managed open-source platform to Airbyte Cloud while preserving pipeline state, configurations, and existing warehouse data. Airbyte has launched a migration service that lets organisations move production workloads from Airbyte Core, its open-source platform, to Airbyte Cloud without rebuilding pipelines or performing costly historical data re-synchronisations. The service is designed to minimise downtime and lower the operational barrier to moving from self-managed infrastructure to the commercial cloud offering. The migration preserves the existing Airbyte workspace, including sources, destinations, connections, schedules, stream configurations, and incremental synchronisation checkpoints. Airbyte Cloud resumes synchronisation from where the self-managed deployment stopped, avoiding months or years of historical data reprocessing. Existing destination warehouse data also remains intact, allowing organisations to continue writing to the same warehouse while avoiding duplicate data loading, additional warehouse compute, API rate-limit issues, and lengthy historical backfills. Connections remain inactive until customers approve final activation, providing a controlled transition and preventing duplicate writes. Airbyte said Airbyte Core will remain free and self-managed, positioning the service as an option for organisations that want to stop operating infrastructure rather than as a move away from open source. "Airbyte Open Source has become the data movement platform of choice for hundreds of thousands of deployments because organizations value flexibility and control. As those deployments grow, many teams decide they'd rather spend time building with data than operating infrastructure. This isn't a push to move anyone off open-source. Airbyte Core stays free and self-managed, and plenty of teams should keep running it," said Michel Tricot, CEO and co-founder of Airbyte. Traditional migrations can require rebuilding connectors and completely re-synchronising historical data, consuming days of engineering effort while increasing API usage and cloud warehouse costs. Airbyte's state-preserving approach removes much of this migration burden.
Airbyte has launched a migration service enabling organisations to move from its open-source platform to Airbyte Cloud without rebuilding pipelines or re-syncing historical data. The service preserves existing workspaces, including sources, destinations, connections, and incremental synchronisation checkpoints, allowing data syncing to resume from where it stopped. The migration maintains complete workspace configurations and existing destination warehouse data, eliminating duplicate data loading and warehouse compute charges. Traditional approaches typically require complete historical synchronisation, consuming days of engineering effort and generating substantial costs. According to a Forrester study, organisations adopting managed Airbyte deployments achieved 239% return on investment. The migration service is available immediately through Airbyte's Solutions Engineering organisation. Airbyte's open-source platform is trusted by 7,000 enterprises for data replication across multi-cloud environments.
Airbyte announces major updates to Airbyte Agents platform. The update from Airbyte also includes expanded write capabilities for HubSpot, so AI agents can now create and update contacts, companies, deals, and tickets. Airbyte has announced major updates to its Airbyte Agents platform, introducing a workspaces feature that provides organisations with governance for organising users, data connectors, and AI agents. The update also includes expanded write capabilities for HubSpot, so now AI agents can create and update contacts, companies, deals, and tickets. Together, these enhancements strengthen Airbyte's position as the data platform for enterprise agentic AI. "With workspaces, organisations can effectively serve many different users with only the access to the data connectors that they require, which provides organisations with a new level of governance for their AI usage," said Michel Tricot, CEO and Co-Founder of Airbyte. "And, the write capabilities added to the HubSpot connector enable AI agents to not just retrieve information, but to take action." New Workspaces Provide Flexibility, Improve Governance The new workspaces feature gives users their own separate instance for Airbyte Agents. Each workspace can have its own users and defined access to specific data connectors with the ability to add and remove users from different workspaces, as needed. Workspaces allow a user or team to create a space, add the connectors that they should have access to, and define which people are members of that space. Only members of a workspace can view and use the connectors within it. Administrators for the organisation can see every workspace regardless, since they need visibility into usage and billing across the platform. This enables departments such as sales, marketing, engineering, and support to share data connectors while restricting access to sensitive systems. Teams can create both shared and private workspaces, ensuring AI agents only access data authorised for that workspace. The Airbyte Agents Context Store - a replicated, search-optimised index - is defined by the data connectors that are used, and specific to each workspace. This provides the agents with optimised data and context that is specific to the workspace. Also, because each workspace maintains its own specific context store, organisations gain stronger governance of data and user access. Workspaces are part of the Airbyte Team plan, which is built for organisations that have multiple users and require single sign-on (SSO) security. The Airbyte Agents Team plan is priced at $299 a month and comes with 10,000 Agent Operations (AOs) a month. Additional AOs beyond the 10,000 a month cost $0.005 per operation. AI Agents Now Write to HubSpot Airbyte has added write operations for HubSpot Agent Connectors. Now, agents can create and update four HubSpot object types: contacts, companies, deals, and tickets. To ensure enterprise-grade governance, organisations maintain complete control over which objects support read and write operations. Permission scopes follow the principle of least privilege, allowing administrators to grant only the access required for each workflow. Building the Enterprise Context Layer for AI Agents These capabilities advance Airbyte's vision of creating the industry's leading data platform for agentic AI. Airbyte provides the foundation for production AI systems by combining enterprise connectivity, indexed contextual understanding, semantic retrieval, governance, and operational execution into a unified platform for AI agents. Sophia Bennett is a news curator at Martechvibe, covering global developments across marketing technology, digital transformation, AI, data, and customer experience trends. View More