Lead Engineer/Data Engineer

Luxoft Spain
Lugo, Spain
2 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Shift work
Languages
English
Experience level
Junior

Job location

Lugo, Spain

Tech stack

API
Azure
Bash
Big Data
Unix
Cloud Computing
Cloud Database
Databases
Data Integration
ETL
Data Transformation
Data Systems
Data Vault Modeling
Data Warehousing
Database Queries
Dimensional Modeling
Elasticsearch
Python
PostgreSQL
SQL Azure
Performance Tuning
Queueing Systems
Windows Shell
Azure
Shell Script
SQL Databases
Informix
Data Storage Management
Cloud Platform System
Data Ingestion
Azure
Database Migration
Containerization
Data Lake
PySpark
Kubernetes
Information Technology
Cosmos DB
Data Management
Physical Data Models
Azure
Data Pipelines
Docker
Databricks
Microservices

Job description

Project descriptionAsegúrese de presentar su candidatura con toda la información solicitada, tal como se expone en la descripción del puesto a continuación.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.ResponsibilitiesWe are looking for 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 solution to current architecture.SkillsReadiness to work until 8.00 pm CET (no need to do overtimes)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 platformsCertification in Azure, Databricks, or other relevant technologies is an added advantageExperience 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 haveExperience with ElasticsearchFamiliarity 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. xbhjioeCollaboration 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 featuresComputer Science and data science academic and education credentialsOtherLanguagesEnglish: B2 Upper Intermediate

Requirements

Readiness to work until 8.00 pm CET (no need to do overtimes) 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. xbhjioe 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 Other Languages English: B2 Upper Intermediate

Apply for this position