Lead Data Engineer​/Data Architect

Project Overview

• The primary goal of the project is the modernization, maintenance and development of an eCommerce platform for a big US-based retail company, serving millions of omnichannel customers each week.
• Solutions are delivered by several Product Teams focused on different domains - Customer, Loyalty, Search and Browse, Data Integration, Cart.
• Current overriding priorities are new brand onboarding, re-architecture, database migrations, and migration of microservices to a unified cloud‑native solution without any disruption to business.

We are looking for a Data Engineer who will be responsible for designing a solution for a big retail company. The main focus is to support processing of big data volumes and integrate the solution into the current architecture.

• Readiness to work, time zone working hours are 9am-6pm CS
• Overall years of experience required 8+ (at least 1+ year in a Lead/Architect position)
• Strong, recent hands‑on expertise with Azure Data Factory and Synapse is a must (3+ years)
• Strong expertise in designing and implementing data models, including conceptual, logical, and physical data models, to support efficient data storage and retrieval
• Strong knowledge of Microsoft Azure, including Azure Data Lake Storage, Azure Synapse Analytics, Azure Data Factory, and Azure Databricks, PySpark for building scalable and reliable data solutions
• Extensive experience with building robust and scalable ETL/ELT pipelines to extract, transform, and load data from various sources into data lakes or data warehouses
• Ability to integrate data from disparate sources, including databases, APIs, and external data providers, using appropriate techniques such as API integration or message queueing
• Proficiency in designing and implementing data warehousing solutions (dimensional modeling, star schemas, Data Mesh, Data/Delta Lakehouse, Data Vault)
• Proficiency in SQL to perform complex queries, data transformations, and performance tuning on cloud‑based data storage
• Experience integrating metadata and governance processes into cloud‑based data platforms
• Certification in Azure, Databricks, or other relevant technologies is an added advantage
• Experience with cloud‑based analytical databases
• Experience with Azure MI, Azure Database for PostgreSQL, Azure Cosmos DB, Azure Analysis Services, and Informatica
• Experience with Python and Python‑based ETL tools
• Experience with shell scripting in Bash, Unix or Windows shell is preferable
• Demonstrated ability to lead cross‑functional engineering teams, define technical strategy and architecture, drive delivery of complex data platforms, mentor engineers, and effectively communicate with stakeholders at all organizational levels

• Experience with Elasticsearch
• Familiarity with containerization and orchestration technologies (Docker, Kubernetes)
• Troubleshooting and performance tuning:
  Ability to identify and resolve performance bottlenecks in data processing workflows and optimize data pipelines for efficient data ingestion and analysis
• Collaboration and communication:
  Strong interpersonal skills to collaborate effectively with stakeholders, data engineers, data scientists, and other cross‑functional teams
• Ability to plan, estimate and track progress of implementing features
• Computer Science and data science academic and educational credentials