Senior Data Engineer

Moontech

About The Position

MoonTech is an AI-first, agentic workflow company transforming influencer performance marketing through autonomous AI agents. Our platform automates the entire campaign lifecycle, enabling brands to launch, manage, optimize, and scale creator campaigns with guaranteed conversions and measurable ROI. Powered by proprietary ground truth data, MoonTech is pioneering the category of conversion intelligence for the creator economy. This is not a role for someone who wants to maintain a finished warehouse and keep existing pipelines running. It is a role for a strong Data Engineer who wants to build the data foundation of an AI-native company from the ground up, where AI agents, product teams, analysts, and commercial teams all depend on the same trusted data layer. You will own the pipelines, warehouse architecture, and core data models behind creator matching, campaign performance, transaction attribution, payouts, and AI-driven decision-making. We are actively modernizing our data infrastructure across GCP and AWS. You will have significant ownership over how that architecture evolves and how our data becomes reliable, observable, and ready for agentic workflows.

Requirements

5+ years of experience building and operating production data pipelines, ideally in a marketplace, e-commerce, affiliate, ad-tech, martech, or high-growth technology company.
Advanced SQL skills and strong hands-on Python experience for building production data pipelines and transformations.
Strong experience with dbt in production and modern cloud data warehouses. Our primary warehouse is BigQuery.
Hands-on experience with an orchestration platform such as Airflow, Dagster, or Prefect, including dependency management, retries, backfills, scheduling, and failure recovery.
Experience operating production infrastructure in GCP, AWS, or both.
Understand how data infrastructure requirements change when the consumers include ML models, embeddings, vector stores, recommendation systems, and autonomous agents.
AI-native by practice, not just interest. You actively use AI coding assistants, LLMs, or agents to accelerate development, investigate failures, generate tests, review transformations, document systems, or automate repetitive engineering work.
Able to explain concretely how AI has changed your engineering workflow and increased your output.
Comfortable working in an evolving environment where schemas, systems, and requirements are still being designed.
Strong production ownership. When a pipeline fails or a number looks wrong, you investigate the full path rather than assuming another team owns the problem.

Nice To Haves

Background in influencer marketing, affiliate marketing, e-commerce, marketplaces, performance marketing, or ad-tech.
Experience with creator, campaign, attribution, conversion, or transaction-level datasets.
Experience building data infrastructure for ML systems, including feature pipelines, embeddings, vector databases, or retrieval systems.
Hands-on experience with entity resolution or identity graphs across systems with inconsistent identifiers.
Experience completing a warehouse, cloud, or major data-platform migration in production.
Experience supporting AI agents or agentic workflows where applications autonomously query, interpret, or act on production data.
GCC or MENA market experience.
Experience working across a hybrid cloud environment is a strong advantage.

Responsibilities

Build and own reliable ingestion, transformation, and enrichment pipelines across high-volume campaign, creator, transaction, product, and commercial data.
Design the core data models consumed by AI agents, product systems, analytics, BI, and machine-learning workflows.
Build enrichment and embedding pipelines that power creator-brand matching, scoring, recommendation, and retrieval workflows.
Solve complex identity and entity resolution problems across multiple external systems where creator, customer, campaign, and transaction identifiers may not agree.
Experience designing stable keys and data models for records arriving from multiple third-party APIs, platforms, and partner systems.
Model commercial data such as creator rates, commissions, payouts, and campaign terms using approaches such as slowly changing dimensions, allowing historical performance to be accurately reconstructed and restated.
Build data quality, freshness, lineage, and pipeline monitoring so issues surface before they reach customers, reports, models, or AI agents.
Treat testing, monitoring, documentation, and observability as part of shipping production infrastructure, not optional cleanup.
Work directly on the migration and consolidation of legacy reporting and data systems into the modern cloud warehouse.
Comfortable designing and executing migrations while maintaining business continuity and validating that historical reporting remains accurate.