Forward Deployment Engineer (AI & Data Platforms)

What You’ll Do

In this role you will:

• Lead technical onboarding of the Teradata Knowledge Platform at design partner and early customer sites — owning the end-to-end deployment, configuration, and validation of AI-assisted data product creation capabilities in real enterprise environments.
• Deploy and configure catalog source connectors at customer sites: Apache Polaris, AWS Glue Data Catalog, Databricks Unity Catalog, Snowflake Horizon, Hive Metastore, and Project Nessie — adapting to each customer’s specific network topology, authentication model, and data estate layout.
• Configure Teradata database and warehouse crawlers targeting customer Vantage and Snowflake estates — defining scan scopes, schedules, credential models, and incremental change detection policies.
• Partner with enterprise customers to configure their Identity & SSO integration: onboard enterprise IdPs (Okta, Azure AD/Entra ID, Ping, ADFS) via SAML 2.0 and OIDC, set up per-user data source credential vaults, and validate end-to-end authentication flows.
• Help customers onboard their business glossaries and data modelling standards onto the Knowledge Platform — importing industry-standard glossaries, seeding the Semantic Data Type vocabulary, and configuring stewardship workflows.
• Translate customer operational feedback, integration failures, and edge-case behaviors into well-documented product and engineering requirements — closing the loop between field deployment and the product roadmap.
• Build and maintain customer-facing deployment playbooks, integration guides, and onboarding accelerators that reduce time-to-value for subsequent deployments.
• Work closely with Data Quality, Graph, Security, and AI Engineering teams to resolve issues surfaced during customer deployments, acting as the technical escalation path between customer and engineering.

Who You’ll Work With

On our team, we:

• Strong experience deploying and integrating enterprise data platforms at customer sites.
• Hands-on familiarity with data catalog tooling (Atlan, Collibra, DataHub, Alation, or equivalent) and how they connect to enterprise data estates.
• Understanding of enterprise identity federation: SAML 2.0, OIDC, and common IdPs (Okta, Azure AD, Ping, ADFS).
• Experience with pipeline tooling — dbt, Airflow, Spark, Fivetran, or equivalent — in production customer environments.
• Strong troubleshooting and root cause analysis skills across distributed data systems.
• Excellent communication and stakeholder management skills — comfortable in both CTO-level architectural conversations and hands-on debugging sessions.
• Ability to operate independently in customer environments with ambiguity, competing priorities, and tight deployment timelines.

What Makes You a Qualified Candidate

• Proven experience designing, developing, and deploying autonomous AI agents at scale.
• Expertise in building agent-based architectures and runtime components for next-generation AI/LLM systems.
• Hands-on experience implementing governance, safety, and compliance in agentic AI environments.
• Ability to partner effectively with product leaders, architects, and platform teams to define enterprise-grade AI capabilities.
• Strong technical ability with intelligent automation, generative AI, and agentic system design.

What You’ll Bring

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 4–7+ years in solutions engineering, forward deployment, or technical customer success at a data platform, data catalog, or enterprise software company.
• Strong SQL skills and direct hands-on experience with cloud data warehouses: Teradata Vantage, Snowflake, BigQuery, or Databricks.
• Experience configuring enterprise IdP integrations via SAML 2.0 and OIDC: Okta, Azure AD/Entra ID, Ping Identity, or ADFS.
• Experience with dbt, Airflow, and OpenLineage or equivalent data lineage standards in production pipeline environments.
• Familiarity with metadata management platforms, graph databases, or data catalog tooling is a strong plus.
• Experience with AI-assisted data tooling, LLM-based products, or agentic workflows is highly valued.
• Strong documentation habits and written communication skills — able to produce precise, customer-facing integration guides, technical runbooks, and deployment playbooks.
• Ability to represent customer needs credibly in engineering conversations — translating field observations into actionable, reproducible …