Senior Data Engineer

Luxoft; Chicago (IL); On-site; USD 110,000 - 170,000; Full time

Job summary

Luxoft is seeking a senior Data Engineer to design, implement and scale data platforms for a large US retailer. You will focus on big data processing, data modeling, and building ETL/ELT pipelines using Azure services.

You will lead cross-functional teams, mentor engineers, and collaborate with stakeholders to ensure reliable, cloud-native solutions and data governance.

Qualifications

• 8+ years of experience in data engineering, with at least 1+ year in a lead/architect role.
• Strong hands-on expertise with Azure Data Factory and Synapse (3+ years).
• Experience designing conceptual, logical, and physical data models.
• Proficiency in SQL and cloud-based data warehouse solutions.
• Experience integrating data from multiple sources and APIs.

Responsibilities

• Design a scalable data solution for a large retail client, focusing on big data processing.
• Build robust ETL/ELT pipelines to load data into data lakes and warehouses.
• Lead cross-functional engineering teams and mentor engineers.
• Collaborate with stakeholders on architecture, governance and delivery.

Skills

Leadership

Stakeholder communication

Data modeling

Mentoring

Problem solving

Education

Azure certification

Tools

Azure Data Factory

Azure Synapse Analytics

Azure Data Lake Storage

Azure Databricks

pyTorch?

Job description

Project description

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 brands onboarding, re‑architecture, database migrations, migration of microservices to a unified cloud‑native solution without any disruption to business.

Responsibilities

• 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 to current architecture.

SKILLS

• Readiness to work until 8.00 pm CET (no overtime required)
• 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 queuing.
• 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 storages.
• 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 Postgres, Azure Cosmos DB, Azure Analysis Services, and Informix.
• 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.

Nice to have

• 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 education credentials