Senior Data Engineer

Moontech; Dubai; On-site; AED 1,250,000 - 2,000,000; Full time

Job summary

MoonTech is seeking a strong Data Engineer to build the data foundation for an AI-native company. You will own pipelines, warehouse architecture, and core data models supporting creator matching, campaign performance, transaction attribution, and payouts.

You will modernize data infrastructure across GCP and AWS, with significant ownership over how architecture evolves and data becomes reliable for agentic workflows.

Qualifications

• 5+ years of experience building and operating production data pipelines in tech marketplaces or ad-tech.
• Advanced SQL and Python for production data pipelines and transformations.
• Strong experience with dbt in production and BigQuery as the primary warehouse.
• Build and own ingestion, transformation, and enrichment pipelines across campaign, creator, and transaction data.
• Design core data models used by AI agents, analytics, and ML workflows.
• Develop enrichment and embeddings pipelines for creator-brand matching and recommendations.
• Handle identity and entity resolution across systems with inconsistent identifiers.
• Experience with stable keys and data models for multi-API data ingestion.
• Model commercial data such as creator rates, payouts, and campaign terms using slowly changing dimensions.
• Hands-on with orchestration platforms (Airflow, Dagster, Prefect) including retries and backfills.
• Implement data quality, freshness, lineage, and monitoring to catch issues early.
• Treat testing and observability as part of production infra shipping.
• Migrate and consolidate legacy data systems into modern cloud warehouse.
• Support migrations while maintaining historical reporting accuracy.
• Experience operating in GCP, AWS, or both, in hybrid environments.
• Understand data infra for ML embeddings, vector stores, and agentic workflows.
• AI-native mindset and ability to explain how AI changes engineering workflows.
• Comfort with evolving schemas and system designs.
• Strong production ownership and end-to-end problem ownership.

Responsibilities

• Own pipelines, warehouse architecture, and core data models for creator matching, campaign performance, and payouts.
• Build reliable ingestion, transformation, and enrichment pipelines for high-volume data.
• Design data models consumed by AI agents, analytics, and BI systems.
• Develop enrichment and embedding pipelines powering matching, scoring, and retrieval workflows.
• Ensure data quality, lineage, freshness, and observability across the data platform.
• Lead migrations and consolidation of legacy systems into a modern cloud warehouse.
• Collaborate with Data Science, BI, Product, and Engineering to shape the data foundation.

Skills

SQL

Python

dbt

BigQuery

Airflow

Data modeling

Data pipelines

Cloud data warehouses

Tools

Airflow

Dagster

Prefect

BigQuery

GCP

AWS

Job description

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.

• 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.

• Hands‑on experience with an orchestration platform such as Airflow, Dagster, or Prefect, including dependency management, retries, backfills, scheduling, and failure recovery.

• 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.

• Experience operating production infrastructure in GCP, AWS, or both. Experience working across a hybrid cloud environment is a strong advantage.

• 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 Have

• 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.

How This Role Fits the Team

You report to the Data Products Lead and work closely with our Data Science, BI, Product, and Engineering teams.

You will own a core part of MoonTech's technical foundation. The data models and pipelines you build will directly power creator‑brand matching, campaign optimization, transaction attribution, creator payouts, business reporting, and the AI agents operating across our platform.

This is a high‑ownership role in a small team. You will not simply receive architecture decisions and implement tickets. You will help decide how the data platform should be designed, what should be rebuilt, what should be retired, and how we create a reliable data foundation capable of supporting MoonTech as an agentic company.