Data Engineer

Malaysiamid

Data EngineerData

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Quick Summary

Overview

About Yaqeen Institute Yaqeen is a nonprofit on a mission to pioneer Islamic research in creative, cutting-edge formats tailored to meet people where they are.

Technical Tools

Data EngineerData

Responsibilities

~1 min read

→Model and structure data for analytical and operational use, owning the transformation layer and its tooling, including dbt
→Design data models that are clear, well documented, and easy for downstream teams to build on
→Define and enforce data contracts and schema standards in collaboration with upstream and downstream engineering teams

Design and build scalable batch and streaming data pipelines across ETL, ELT, and change data capture patterns
Orchestrate and schedule workflows using tools such as Airflow, Dagster, or Prefect
Ensure pipelines are observable, recoverable, and well instrumented, with monitoring and data quality checks that catch issues before they reach consumers

Architect and optimize our modern data warehouse, including storage layer design, table formats, and compute and query engine integration
Provision and maintain cloud infrastructure using infrastructure as code, and manage deployment and container orchestration
Implement privacy, anonymization, and governance controls to keep sensitive data protected and compliant

Participate in planning and requirements discussions to raise data quality and modeling concerns before development starts
Translate requirements from analysts, researchers, and product engineers into robust data models and reliable delivery
Keep engineering and the wider organization connected through transparent, timely communication

Requirements

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Advanced SQL, including performance tuning and complex analytical query design
Advanced Python for pipeline development, tooling, and automation
Familiarity and hands-on experience with AWS, Azure, GCP, AliCloud, or an equivalent
Data warehousing: expert knowledge of one modern platform (Snowflake, BigQuery, Databricks, or Redshift), including architecture, storage layer design, table formats, and compute and query engine integration
Experience developing and orchestrating data pipelines across ETL, ELT, CDC, and stream processing
Data modeling and tooling expertise, including dbt
Experience with modern orchestration tools like Airflow, Dagster, or Prefect
Working knowledge of cloud infrastructure tooling, including Terraform, Helm, and Kubernetes or equivalent
Experience defining and enforcing data contracts or schema standards in collaborative engineering environments
Exposure to privacy and anonymization implementations
Background in a SaaS, startup, or product-led environment is a plus
Familiarity with data observability and lineage tooling such as Sentry or Datadog is a plus