Telmai
Automated data observability and quality for Data Lake
Forward-Deployed Customer Engineer (Enterprise)
About the role
About the Role
Telmai customers move through three stages after purchase:
- Install in their VPC,
- Configure data sources and catalogs,
- Adopt profiling, monitors, and automated validation.
This role is responsible for accelerating all three for large enterprise customers. You will be the technical owner who ensures Telmai is deployed, integrated, and delivering value fast.
What You’ll Do
Implementation & Activation
- Guide customers through automated deployment in AWS, Azure, or GCP
- Provide hands-on cloud/network/security support when needed
- Integrate Telmai with customer ecosystems (GCS, S3, ADLS; BigQuery, Snowflake, Databricks; Atlan, Alation, Collibra; Iceberg, Delta, Hudi)
- Configure assets, profiling, rules, lineage, and validation flows
- Build small utilities or synthetic datasets to speed onboarding
Data Reliability Outcomes
- Enable automated validation across landing → transform → consumption layers
- Diagnose issues such as schema drift, null spikes, volume anomalies, invalid values, or rule failures
- Work with engineering, analytics, governance, and business teams to align on quality goals
- Demonstrate improvements in data reliability and trust
Customer Leadership
- Run workshops, stand-ups, and weekly sessions
- Navigate enterprise org structures and unblock cross-team dependencies
- Communicate clearly with both technical and executive stakeholders
- Act as the primary technical owner for post-sales success
Influence Product
- Bring deployment insights back into product and engineering
- Inform roadmap areas (validation automation, lakehouse integrations, AI-agent workflows)
- Help build playbooks and best practices for future implementations
What We’re Looking For
Background & Skills
- 4–6 years in a customer-facing engineering role (FDE, SA, CE, consulting engineer)
- Strong data architecture and pipeline fundamentals
- Hands-on cloud experience (AWS, GCP, or Azure)
- Experience with lakehouse/warehouse ecosystems
- Familiarity with data lakes, cloud storage, catalogs, or metadata systems
- Comfortable leading multi-stakeholder enterprise conversations
Traits & Mindset
- AI-first and highly adaptable
- Customer-obsessed and outcome-driven
- High ownership, bias for action, and low-ego collaboration
- Comfortable with ambiguity and fast-moving startup environments
Nice-to-Haves
- Experience with DQ/observability or metadata platforms
- Iceberg/Delta/Hudi familiarity
- Python/SQL for quick utilities
- Exposure to regulated industries (FSI, healthcare, insurance)
Why Join Telmai
- Founding impact: Help shape Telmai’s customer engineering function
- High visibility: Work directly with the founders
- Enterprise momentum: Deploy Telmai at brands like McDonald’s and Fortune 500s
- AI + Data inflection point: Build on top of Telmai’s new Data Reliability Agent
- Low bureaucracy: High trust, fast decisions, real ownership
About the interview
Intial meeting with hiring manager to understand interest and technical skills
Meeting with Sales Engineer for customer management skills
Interview with both founders and small working exercise.
About Telmai
Telmai is a no-code data quality analysis and observability platform that helps data teams quickly detect and investigate data quality issues. Our early clients like Dun and Bradstreet are able to identify data quality issues across millions of records within minutes instead of hours/days.
We 're a tight-knit team funded by Zetta Venture Partners, .406 Ventures and Y combinator. This role is remote (USA).
