Вакансия Senior Data Engineer (with ETL expertise)

Skills

• ETL
• ELT
• SQL
• T-SQL
• Python
• Java
• Azure
• AWS
• GCP

Описание вакансии

Innovecs is a global digital services company with a presence in the US, the UK, the EU, Israel, Australia, and Ukraine. Specializing in software solutions, the Innovecs team has experience in Supply Chain, Healthtech, Collaboration Tech, and Gaming.
For the fifth year in a row, Innovecs is included in the Inc. 5000, the list of fastest-growing private companies in the US, and a ranking of the best global outsourcing service providers by IAOP. Recently, Innovecs was honored with the prestigious Global Good Awards for the Employee Engagement & Wellbeing, won gold at the Employer Brand Management Awards, and was included in the Global Top 100 Inspiring Workplaces Ranking.

Requirements

• 5+ years of experience as a Data Engineer or in a similar backend engineering role.
• Proven experience building production-grade data pipelines (ETL/ELT).
• Strong proficiency with SQL and TSQL in conjunction with Python or Java.
• Solid understanding of cloud platforms (e.g.,Azure AWS, or GCP).
• Strong grasp of data modeling, data architecture, and data governance principles.
• Excellent problem-solving skills and the ability to work independently in a fast-paced environment.
• Strong knowledge of database normalization, indexing strategies, and execution plans.
• Experience in Supply Chain & Logistics domain would be preferred.

Responsibilities

• Design and implement robust, scalable, and high-performance data pipelines to ingest, process, and serve structured and unstructured data from various internal and external sources (e.g., TMS, WMS, ERP, telematics, IoT).
• Optimize system performance and scalability for large logistics and operational data volumes.
• Build and maintain data models, data marts, and data warehouses/lakes that support analytics and decision-making.
• Partner with the team to translate business requirements into technical data solutions.
• Implement data quality, lineage, and observability practices.
• Drive ETL/ELT best practices, automation, and operational excellence in data workflows.
• Ensure security, compliance, and privacy standards in data processing.
• Take ownership of production support.