# Software Engineer, Growth Data Platform at Omni > Remote · United States · Unspecified · Data engineering · open. Source: https://alderhq.app/jobs/ashby%3Aomni%3Ade8286db-3605-4e37-a41d-84979d8d0ae8 (read 2026-11-06). ## Facts (observed) - Company: [Omni](https://alderhq.app/companies/omni) - Location text: San Francisco, CA; United States - Where it can be done from (inferred): Remote · United States (evidence: "Location: San Francisco, CA; United States") - Posted: 2026-09-08 (59 days ago) - First seen by alder: 2026-11-02 · last verified: 2026-11-04 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://jobs.ashbyhq.com/omni/de8286db-3605-4e37-a41d-84979d8d0ae8 - Skills listed (inferred): Python, AWS, Terraform, Spark, Airflow, Snowflake, dbt, SQL ## Pay and sponsorship - Posted pay: $170k–$250k per year (from the listing's pay field) - Market band for similar posted roles (SF Bay Area, n 51): p25 $180k, median $200k, p75 $266k ## Description (the employer's text) ### About Omni Omni is the AI analytics platform that turns company data into a trusted source of truth for AI. Built on a semantic model, Omni gives teams a governed context graph, so everyone gets the same trusted answer. Ask in plain English, refine in workbooks, or bring your data into tools like Claude, ChatGPT, Cursor, and Slack. Our team has spent decades building data platforms, and we started Omni to fix problems we saw firsthand. Today, we help customers like Condé Nast, BambooHR, and Synthesia get better answers from their data. We're headquartered in San Francisco and have raised $217M from top investors including ICONIQ, Theory Ventures, and Redpoint Ventures. Our latest $120M Series C valued us at $1.5B. If you like helping people get more from their data, come build with us. We’d love to hear from you. ### Responsibilities Build and scale the data platform that turns messy GTM signals into reliable, auditable workflows Design queues, and workers for enrichment, routing, assignment, and activation Integrate external data sources and APIs across CRM, sales engagement, ads, first and third party intent, and enrichment vendors Work directly with various GTM teams to turn one-off experiments into reusable platform primitives Debug production issues across Snowflake, dbt, Dagster, AWS, vendor APIs, webhooks, and downstream business systems ### About You 2+ years of experience building data platforms, backend systems, workflow engines, or data / ML infrastructure Deep experience with analytical data systems such as Snowflake, Databricks, BigQuery, dbt, Spark, Airflow, Dagster, or similar tools Track record of owning complex, end-to-end systems that serve real users or business-critical workflows Clear judgment about data modeling, schema evolution, contracts, lineage, and long-term maintainability You care whether the workflow actually works, whether customers trust it, and whether downstream teams can explain the output Strong debugging instincts ### Tech Stack You are not expected to have experience with every tool listed below, but you should be excited to work across data infrastructure, backend services, and orchestration. Snowflake as the warehouse and operational data store for modeled data, queues, workflow state, and activation outputs dbt for transformation, data models, and tests Dagster for orchestration of scheduled jobs, dependency-aware workflows, and operational retries AWS Batch and Lambda for custom workers, vendor enrichment jobs, webhook handling, and stateful background processing Fivetran Python and SQL Terraform and GitHub Actions Soda Omni Location This role allows for a hybrid schedule in our SF headquarters. If you are based anywhere else in the US, the role is remote. Omni is an equal opportunity employer. We value diversity and encourage you to apply even if you don’t check every single box. Please, let us know if you need any reasonable accommodations during the interview process. --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Aomni%3Ade8286db-3605-4e37-a41d-84979d8d0ae8 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.