Data Engineer (Databricks)

Colombiamid

Data EngineerData

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Quick Summary

Overview

We are:Wizeline, a global AI-native technology solutions provider, develops cutting-edge, AI-powered digital products and platforms. We partner with clients to leverage data and AI,

Technical Tools

Data EngineerData

Responsibilities

~1 min read

→Design, develop, and maintain scalable data pipelines using ETL/ELT processes.
→Develop and implement data integration workflows to meet operational business needs.
→Write robust scripts and complex SQL queries to extract, transform, and load data from multiple sources.
→Design, optimize, and maintain data pipelines and architectures, ensuring data accessibility, cleanliness, and reliability.
→Contribute to the design and implementation of data warehouse architectures and data lakes.
→Support stream and batch data processing workflows.
→Work with relational and non-relational databases, ensuring efficient design and optimization.
→Apply data modeling techniques to support data consumption and analytics requirements.
→Ensure data quality, consistency, and compliance with data governance and security practices.
→Collaborate with software engineers and data consumers to align data integration workflows with business needs.
→Apply version control and CI/CD principles to support reliable data development and deployment.

At least 4 years of professional experience as a Data Engineer.
Proven experience designing, building, and maintaining scalable data pipelines and ETL/ELT processes.
Experience developing data integration workflows and working with multiple data sources.
Experience designing, optimizing, and maintaining data architectures and pipelines.
Experience with relational and non-relational database design and optimization.
Experience with data pipeline orchestration and workflow management.
Experience with cloud infrastructure and data security and governance best practices.
Advanced SQL.
Azure and Databricks.
Data processing using batch and stream processing paradigms.
Version control and CI/CD principles.
Data quality, consistency, and reliability practices.

AI Tooling Proficiency: Leverage one or more AI tools to optimize and augment day-to-day work, including drafting, analysis, research, or process automation. Provide recommendations on effective AI use and identify opportunities to streamline workflows.