Desktop Engineer

Job Description

"Data Platform & Architecture Development

•Design, implement, and maintain scalable data platforms for efficient data storage, processing, and retrieval.

•Build cloud-native and distributed data systems that enable self-service analytics, real-time data processing, and AI-driven decision-making.

•Develop data models, schemas, and transformation pipelines that support evolving business needs while ensuring operational stability.

•Apply best practices in data modeling, indexing, and partitioning to optimize query performance, cost efficiency, considering best practices for Sustainability.
ETL, Data Pipelines & Streaming Processing

•Build and maintain highly efficient ETL pipelines using SQL, Python, to process large-scale datasets.

•Implement real-time data streaming pipelines using Kafka, Apache Beam, or equivalent technologies.

•Develop reusable internal data processing tools to streamline operations and empower teams across the organization.

•Write advanced SQL queries for extracting, transforming, and loading (ETL) data with a focus on execution efficiency.

•Ensure data validation, quality monitoring, and governance using automated processes and dashboards.
MLOps & Cloud-Based Data Infrastructure

•Deploy machine learning pipelines with MLOps best practices to support AI and predictive analytics applications.

•Optimize data pipelines for ML models, ensuring seamless integration between data engineering and machine learning workflows.

•Work with cloud platforms (GCP) to manage data storage, processing, and security.

•Utilize Terraform, Jenkins, CI/CD tools to automate data pipeline deployments and infrastructure management.
Collaboration & Agile Development

•Work in Agile/DevOps teams, collaborating closely with data scientists, software engineers, and business stakeholders.

•Advocate for data-driven decision-making, educating teams on best practices in data architecture and engineering."

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•5+ years of experience as a Data Engineer working with large-scale data processing.

•Strong proficiency in SQL for data transformation, optimization, and analytics.

•Expertise in programming languages (Python, Java, Scala, or Go) with an understanding of functional and object-oriented programming paradigms.

•Experience with distributed computing frameworks.

•Proficiency in cloud-based data engineering on AWS, GCP, or Azure.

•Strong knowledge of data modeling, data governance, and schema design.

•Experience with CI/CD tools (Jenkins, Terraform) for infrastructure automation."

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•Experience with real-time data streaming (Kafka, or equivalent).

•Strong understanding of MLOps and integrating data engineering with ML pipelines.

•Familiarity with knowledge graphs (including Neo4j tool) and GraphQL APIs for data relationships.

•Background in retail, customer classification, and personalization systems.

•Knowledge of business intelligence tools and visualization platforms."

Qualifications:
Qualifications

Degree required

Additional Information

All your information will be kept confidential according to EEO guidelines.