Senior Data Platform Engineer

Role Overview

Senior Data Platform Engineer at Ziina, based in or requiring relocation to the UAE.

Role Purpose

Build the foundations of Ziina's data platform from the ground up — the pipelines, warehouse, and tooling that will power decision-making, analytics, and future ML/AI capabilities across the company. Own systems end-to-end in a fast-paced, agile environment while making sound tradeoffs and focusing on high-impact solutions.

Key Responsibilities

Data Pipelines & Ingestion

• Build and maintain scalable data pipelines and processing systems that ingest data from transactional databases, object storage, and external APIs.
• Design ETL/ELT workflows supporting data ingestion from PostgreSQL, S3, and external APIs.
• Support both transactional and event-driven use cases.

Data Warehouse & Modeling

• Design and evolve data warehouse architecture and data models to support analytics, reporting, and ML/AI use cases.
• Build data warehouse architectures and models that scale cleanly as the company grows.

Reliability & Performance

• Improve the reliability, observability, and performance of data systems.
• Implement data quality checks and monitoring to ensure trusted, accurate data across the company.
• Maintain familiarity with data quality, observability, and monitoring practices for production data systems.

Analytics & Self-Service Access

• Enable self-service data access and analytics so product, engineering, and business teams can make data-driven decisions independently.
• Build self-service analytics tooling that meaningfully expands who in the company can work with data.

Machine Learning & AI

• Support the foundational capabilities that will power ML and AI use cases as they grow.
• Enable ML/AI workloads on top of the data platform — feature stores, ML pipelines, and serving infrastructure.

Qualifications & Experience

• 5+ years of experience in Data Engineering, Data Platform, or Analytics Engineering.
• Strong expertise building and maintaining data pipelines with orchestration tools such as Dagster, Airflow, or Prefect.
• Proficiency with modern data warehouses including Snowflake, BigQuery, or Redshift.
• Hands-on experience with transformation frameworks like dbt.
• Hands-on experience ingesting data from a mix of sources — PostgreSQL, S3, external APIs — supporting both transactional and event-driven use cases.
• Proven experience building data platforms at fintech or other high-scale, high-reliability companies.
• Track record of designing data warehouse architectures and models that scaled cleanly as the company grew.
• History of building self-service analytics tooling that meaningfully expanded who in the company could work with data.
• Experience enabling ML/AI workloads on top of a data platform — feature stores, ML pipelines, or serving infrastructure.

Skills & Competencies

• Write ETL/ELT workflows in Python.
• Use of the latest AI tools and technologies to boost productivity.
• Active engagement in the data community through open source contributions, conference speaking, or technical writing.

Additional Information

Data Platform Tech Stack

• Snowflake as the data warehouse, with dbt for data transformation and modeling.
• Dagster for orchestrating data pipelines, with Python for building ETL/ELT workflows.
• Data ingestion from PostgreSQL, S3, and external APIs, supporting both transactional and event-driven use cases.
• Metabase for analytics and internal reporting.

Broader Ziina Engineering Tech Stack

• Typescript, Node.js, and Nest.js for the main application's backend.
• GraphQL Federation for client-facing APIs and Kafka for inter-service communication.
• PostgreSQL for consistent and durable storage, Redis for quick fetching, and Elasticsearch for quick searching.
• AWS for hosting cloud infrastructure and Kubernetes for orchestrating workloads.
• Terraform for Infrastructure as Code and GitHub Actions for CI/CD.