Staff Data Engineer (Remote)

Joblet-AI; United Arab Emirates; Remote; AED 734,000 - 955,000; Full time

Benefits offered by this job

Equity compensation

Remote-first workforce

Health insurance reimbursement

Job summary

micro1 is seeking a staff-level data engineering leader for a remote-first role. You will design and own our data warehouse, ingestion pipelines, transformations, and analytics foundations to enable self-service analytics and AI-driven decision making.

You will mentor engineers, establish data engineering standards, and drive architecture decisions while ensuring reliability and scale across the data platform. Equity and performance-based bonuses apply.

Qualifications

• Staff-level engineering with hands-on data platform production experience.
• Led greenfield data platform from scratch.
• Deep knowledge of data warehouses and ETL/ELT.
• Proficient in SQL and Python fundamentals.
• Experience with modern data tools listed above.

Responsibilities

• Build our data platform from zero: Architect and implement internal data warehouse, ingestion infrastructure, ETL/ELT pipelines, and core data models.
• Create agent-ready data foundations: Design reliable datasets and interfaces for AI agents to discover, query, and use.
• Turn messy data into trusted systems: Transform fragmented data into consistent, high-quality sources of truth.
• Enable self-service analytics: Build infrastructure for teams to answer questions independently.
• Own product analytics infrastructure: Establish event tracking, instrumentation, data models, and reporting foundations.
• Automate data operations: Leverage AI coding agents and automation for pipeline development, validation, documentation, monitoring, and troubleshooting.
• Build for reliability and scale: Establish data quality, observability, governance, and scalable patterns.
• Raise the engineering bar: Mentor engineers and lead architectural decisions.

Skills

SQL

Python

Data warehouse

ETL/ELT

Airflow

dbt

Dagster

Data modeling

Data quality

Observability

Tools

Snowflake

BigQuery

Redshift

Databricks

dbt

Airflow

Dagster

Job description

• Role Type: Full-time
• Location: Remote

The Role

This is a Staff-level, hands-on engineering role for someone who has built data infrastructure from zero to production and knows how to turn fragmented operational data into reliable, accessible, and actionable systems.

You’ll own the architecture and implementation of our data warehouse, ingestion pipelines, transformations, and product analytics foundations.

We’re building an AI-native organization where both humans and autonomous agents need access to trustworthy, well-structured data. You’ll design the systems that make this possible, enabling self-service analytics, automated decision-making, and increasingly intelligent workflows across the company.

What You’ll Do

• Build our data platform from zero: Architect and implement our internal data warehouse, ingestion infrastructure, ETL/ELT pipelines, and core data models.
• Create agent-ready data foundations: Design reliable, well-documented datasets and interfaces that AI agents can safely discover, query, and use.
• Turn messy data into trusted systems: Transform fragmented operational and product data into consistent, high-quality sources of truth.
• Enable self-service analytics: Build the infrastructure that allows engineering, product, and business teams to answer questions independently without relying on manual data requests.
• Own product analytics infrastructure: Establish the event tracking, instrumentation, data models, and reporting foundations needed to understand product usage and performance.
• Automate data operations: Leverage AI coding agents and intelligent automation to accelerate pipeline development, data validation, documentation, monitoring, and troubleshooting.
• Build for reliability and scale: Establish data quality standards, observability, governance, and scalable architectural patterns from the beginning.
• Raise the engineering bar: Mentor engineers, establish strong data engineering practices, and lead architectural decisions through hands-on execution.

What We’re Looking For

• Staff-level engineering expertise with extensive hands‑on experience building and operating production data platforms.
• Proven greenfield experience: You’ve personally led the design and implementation of a data platform from scratch, rather than exclusively maintaining or extending existing infrastructure.
• Deep understanding of data warehouses, ETL/ELT architectures, data modeling, orchestration, and distributed data processing.
• Strong proficiency in SQL and Python, with the software engineering fundamentals needed to build reliable data infrastructure.
• Experience with modern data technologies such as Snowflake, BigQuery, Redshift, Databricks, dbt, Airflow, Dagster, or equivalent tools.
• Strong understanding of data quality, lineage, observability, access controls, and production data reliability.
• Experience designing analytics infrastructure that supports product insights and operational decision-making.
• AI-native engineering habits: You already use AI coding agents as part of your daily workflow and actively automate repetitive development and data engineering tasks.
• Ability to make pragmatic architectural decisions, balancing scalability, simplicity, performance, and engineering velocity.
• Demonstrated technical leadership, including mentoring engineers and establishing engineering standards.

Nice to have

• You’ve been the founding or early Data Engineer responsible for building a company's first real data platform.
• You’ve designed data systems that support both traditional analytics and AI-powered applications or autonomous agents.
• You have experience building semantic layers, data discovery systems, or structured interfaces that make enterprise data accessible to AI.
• You’ve automated substantial portions of data pipeline development, validation, or maintenance using AI agents.
• You know how to avoid overengineering while building foundations that won’t need to be replaced six months later.
• You’re comfortable moving between data architecture, production pipelines, analytical modeling, and hands‑on debugging.

Compensation & Benefits Notice

The national pay range for this full-time position is base salary of $200,000 –$260,000. All employees are eligible for equity compensation, and employees may also receive performance-based bonuses, dependent on role and subject to company policies. micro1 provides a comprehensive benefits package, including up to 100% reimbursement for health‑insurance premiums, paid time off, a 401(K) plan with a company match, and additional benefits designed to support a high‑performing, remote‑first workforce.

Equity & Diversity

micro1 is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, veteran status, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation during the application process, reach out to [e-mail].

Hiring Process

Our hiring process utilizes artificial intelligence tools to assist in candidate screening and assessment. Our AI tools are designed to complement, not replace, human decision‑making.

What You’ll Do (duplicate)

• Build our data platform from zero: Architect and implement our internal data warehouse, ingestion infrastructure, ETL/ELT pipelines, and core data models.
• Create agent-ready data foundations: Design reliable, well-documented datasets and interfaces that AI agents can safely discover, query, and use.
• Turn messy data into trusted systems: Transform fragmented operational and product data into consistent, high‑quality sources of truth.
• Enable self-service analytics: Build the infrastructure that allows engineering, product, and business teams to answer questions independently without relying on manual data requests.
• Own product analytics infrastructure: Establish the event tracking, instrumentation, data models, and reporting foundations needed to understand product usage and performance.
• Automate data operations: Leverage AI coding agents and intelligent automation to accelerate pipeline development, data validation, documentation, monitoring, and troubleshooting.