Senior Data Engineer – Amazon Redshift Expert

We are looking for a senior-level data engineer who brings both hands-on Redshift depth and strong ownership. You should be comfortable working across Redshift provisioned or Serverless environments and implementing improvements end-to-end—from data modeling to pipeline orchestration and workload tuning.

Experience : 5+ years in data engineering with deep, hands-on Amazon Redshift expertise.

SQL : Advanced SQL and proven query tuning experience on large datasets.

AWS ecosystem : Hands-on experience with Amazon Redshift Serverless , Amazon S3 , AWS Glue , AWS Lambda , AWS IAM , and Redshift Spectrum .

ETL/ELT & orchestration : Experience building ETL/ELT pipelines using Apache Airflow , dbt , or similar tooling.

Warehouse practices : Practical experience with Workload Management , data modeling , and data warehousing .

Communication : C1 English level or higher.

We value ownership and clarity. You should be able to independently investigate issues, propose solutions, and communicate trade-offs to stakeholders. We also value structured documentation, strong engineering judgment, and a reliability mindset—designing changes that improve performance and resilience without creating operational risk.

Bonus : Experience in fintech or other regulated data environments is valuable, especially when designing for governance, traceability, and consistent data quality.

We will need a Senior Data Engineer with deep Amazon Redshift expertise to take ownership of the performance and reliability roadmap for the data warehouse. You will assess the current Redshift landscape, diagnose bottlenecks, and implement optimizations that directly improve query latency, system stability, and cost efficiency.

Review the current Redshift environment and identify issues related to performance , cost , reliability , and data model design .

Design and tune warehouse structures, including distribution keys , sort keys , compression strategy , and Workload Management (WLM) configuration.

Build and maintain robust ETL/ELT pipelines loading data into Redshift using the AWS data stack and appropriate orchestration (e.g., Airflow).

Optimize slow queries and continuously reduce provisioned or Serverless Redshift costs without sacrificing reliability.

Develop and evolve data models that support analytics and BI use cases, ensuring query-friendly structures and consistent semantics.

Document technical decisions, findings, and implemented changes so the team can reuse patterns and maintain long-term maintainability.

We will expect you to operate independently with stakeholders, translating business and analytics needs into practical technical improvements with measurable outcomes within a short-term engagement.

Experience improving Redshift concurrency and workload isolation beyond basic tuning (e.g., advanced WLM strategies).

Track record of reducing warehouse costs through architecture and query improvements while maintaining SLAs.

Experience working with regulated datasets (fintech, payments, or similar), including strong data handling and documentation habits.

Proficiency with additional AWS analytics services that complement Redshift pipelines (e.g., streaming/real-time patterns) and strong CI/CD practices for analytics code.

We provide the opportunity to participate in impactful and technically rigorous industrial data projects that drive innovation and professional growth. Our work environment emphasizes technical excellence, collaboration, and continuous innovation.

Niuro supports a 100% remote work model , allowing flexibility in work location globally. We invest in career development through ongoing training programs and leadership opportunities, ensuring continuous growth and success.

Upon successful completion of the initial contract, there is potential for long-term collaboration and stable, full-time employment , reflecting our long-term commitment to our team members.

We will also provide strong administrative support infrastructure so you can focus on delivering exceptional results.