Deploy Engineer

Role Description

Working at the intersection of data engineering, marketing technology, and enterprise account management, you will be responsible for onboarding enterprise clients onto Ometria's agentic intelligence product, ΛTLΛS. This will range from identifying and connecting data sources through to training clients and ensuring the platform delivers ongoing value.

You will act as a trusted technical partner for our clients, guiding them through data integration, validation, alerting, and documentation strategies. This is a deeply hands-on role that requires both strong technical capability and the ability to communicate clearly with enterprise stakeholders. You'll be part of a team of strategic retail marketing experts empowering leading enterprise businesses to harness the power of AI-driven intelligence.

Key Outcome

• A successful onboarding is a client who is delighted with ΛTLΛS and has proven ROI from it.

Sub-outcomes

• Successful client onboarding: Every enterprise client is fully onboarded onto ΛTLΛS, with data sources identified, connected, and validated within agreed timelines.
• Clients understand their data landscape within the platform and are equipped to use it effectively from day one.
• Data integrity and platform reliability: Platform inputs and outputs are continuously monitored, with alerts addressed promptly to maintain data integrity and client confidence.
• Clients have robust QA and validation strategies in place, co-developed with their internal teams.
• Client enablement and self-sufficiency: Clients are trained and confident in using ΛTLΛS, reducing day-to-day dependency on Ometria.
• Clients have custom connectors, integrations, and documentation strategies that are maintained and scalable.
• Trusted technical partnership: Clients see you as a trusted technical advisor, with strong relationships across their QA, data, marketing, and technology teams.
• Positive client sentiment driven by proactive communication, expert guidance, and reliable delivery.

Key Responsibilities

• Data Source Identification and Integration:
  • Identify and assess the data sources relevant to each client's ΛTLΛS deployment, determining the most effective approach for connecting to each.
  • Assist clients in building custom connectors and data integrations, including working with data warehouses such as Snowflake and Databricks.
  • Support context gathering and data uploading into the ΛTLΛS product, ensuring completeness and accuracy.
• Semantic Review and Data Validation:
  • Review semantic inputs and outputs for the ΛTLΛS product, ensuring data is accurately interpreted and surfaced.
  • Work with clients' QA and validation teams to develop and implement robust data validation strategies.
  • Monitor the platform's inputs and outputs on an ongoing basis, identifying and addressing any alerts or anomalies.
• Client Enablement and Training:
  • Train clients on the usage of the ΛTLΛS platform, tailoring sessions to different audiences including technical and marketing stakeholders.
  • Work with clients to define alerting and notification strategies that keep the right people informed at the right time.
  • Support clients in developing and maintaining their documentation strategy, ensuring knowledge is captured and accessible.
• Enterprise Client Engagement:
  • Act as a trusted technical advisor throughout the client lifecycle, building strong relationships with data, marketing technology, and QA teams.
  • Communicate technical concepts clearly and confidently with enterprise stakeholders, adapting your approach to the audience.

Qualifications

• Minimum 5 years of technical account management or solutions architecture experience in an enterprise software organization, preferably in the Retail and Ecommerce space.
• Hands-on experience with data warehousing platforms, particularly Big Query, Snowflake and Databricks, including the ability to build and manage data integrations and custom connectors.
• Excellent communication and stakeholder management skills, with the ability to engage confidently with enterprise clients across both technical and commercial functions.
• Strong understanding of the marketing technology landscape — familiarity with the major platforms, what they are used for, and how data flows between them.
• Experience in marketing programme management and client data management, with a solid grasp of how brands structure and leverage their marketing data.
• Practical knowledge of identity resolution and CDP-type activities, including data unification, audience segmentation, and profile management.
• AI engineering capability, with experience building or working with AI-driven data products, pipelines, or platforms.
• A methodical, self-directed approach to work, with the ability to manage multiple client engagements simultaneously and prioritise effectively.

Requirements

• The salary range for this role is $150,000 to $200,000. The final pay offered may vary based on several factors, such as job-specific knowledge, skills, and experience.

Benefits

• Unlimited paid time off
• Health Insurance
• Dental
• Vision
• Mental Health Support