Стек и навыки

Redshift
ETL
AWS
Data Modeling
SQL
Airflow
Data Warehouse
Amazon Redshift Serverless
Amazon S3
AWS Glue
AWS Lambda
IAM

Key Responsibilities

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.

Nice to Have

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.

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

Benefits

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.

We are Niuro, connecting projects with elite tech teams and partnering with leading U.S. companies to simplify global talent acquisition. In this engagement, we will empower an autonomous, high-performance data engineering team to improve a cloud-based analytics platform centered on Amazon Redshift. You will work on performance, reliability, and cost-efficiency initiatives across the Redshift ecosystem, including workload optimization and scalable data modeling. The goal is to deliver measurable improvements for analytics and business intelligence teams, while strengthening governance through clear technical documentation, repeatable best practices, and dependable ETL/ELT pipelines across the AWS data stack.