Senior Data Engineer (AI)

Location: Remote, United States; other listed locations are New York, New York, and San Francisco, California.

Salary: $190K–$220K annually, plus equity.

Company

hirify.global builds developer-focused video infrastructure for encoding, streaming, playback, delivery, monitoring, analytics, and video understanding.

What you will do

• Own the end-to-end data platform, including Snowflake, ingestion, dbt transformations, permissions, CI/CD, cost management, orchestration, deployments, environments, and incident response.
• Build and maintain ingestion from production systems and business tools, and push curated data to Salesforce, NetSuite, and HubSpot.
• Build semantic models and the data foundation used by internal AI agents to answer business questions, and measure answer correctness.
• Improve data quality, testing, observability, contracts, governance, and maintainability while reducing technical debt.
• Use coding agents such as Claude Code and Cursor for pipeline and dbt development, and define standards for access, review, and human verification of AI-generated work.
• Partner with Engineering, Finance, FinOps, Product, and GTM on source data changes, business questions, model priorities, and engineering planning.

Requirements

• 5+ years of data engineering experience with a strong software engineering foundation.
• Production experience building and operating a data stack end to end across ingestion, warehouse, transformation, orchestration, and ongoing operations.
• Deep hands-on dbt experience, including model design, testing, macros, CI, and refactoring large projects.
• Hands-on administration of a cloud data warehouse, preferably Snowflake; BigQuery or Databricks are considered, including permissions, performance tuning, and cost management.
• Experience with data contracts and observability tools such as Metaplane, Monte Carlo, or Elementary.
• Expert SQL, strong data modeling skills, dimensional modeling, grain, and slowly changing dimensions.
• Strong Python for pipelines, automation, and tooling; experience with managed ingestion tools such as Fivetran or Airbyte; and comfort with AWS or GCP.
• Regular use of AI coding tools, with the ability to identify where they help and where their output is unreliable.
• Working knowledge of analysis and statistics, including extracting, sanity-checking, and clearly presenting answers to business questions.
• Commitment to data quality, governance, maintainability, and long-term reliability.

Nice to have

• Semantic layer experience with dbt Semantic Layer, MetricFlow, Cube, Omni, LookML, or similar tools.
• Experience building or integrating LLM agents over structured data, including text-to-SQL, MCP servers, or retrieval over metric definitions, and designing evaluations for LLM output quality.
• Data science experience in forecasting, regression, cohort or churn modeling, or experiment analysis.
• Experience with vector, time-series, or transactional databases, streaming systems such as Kafka or Kinesis, and caches such as Redis.
• Reverse ETL into Salesforce, NetSuite, or HubSpot; finance data experience; or experience with developer tools, API-first, infrastructure, video streaming, or CDN companies.
• Experience at a smaller company with broad ownership.

Culture & Benefits

• Supportive culture focused on excellent work, work-life balance, inclusion, and making colleagues better.
• U.S. benefits include flexible vacation, 11 company holidays, weekly no-meeting days, quarterly focus weeks, health, dental, and vision insurance, FSA/HSA, disability and life insurance, 401(k), paid parental leave, and professional development.
• Additional benefits include travel accident insurance and lunch, internet, and ancillary equipment reimbursements.
• Office spaces are available in Downtown San Francisco, New York City, and London.