Koantek

Data Engineer- Multiple Positions

Vaga remota de Data Engineering com fit claro de localização do candidato.

Publicada19 de jul. de 2026

Países elegíveis1 país aceito

Sinal de senioridadeMiddle

Modelo de trabalhoRemoto

Locais aceitos para candidatos

Estados Unidos

AWS Azure CI/CD GCP Python Spark SQL

Resumo da vaga

Data Engineer- Multiple Positions

Requisitos e responsabilidades

Conteúdo da vaga extraído em seções para revisão mais rápida.

The impact you will have:

Guide Big Data Transformations:Implementation of comprehensive big data projects, including the development and deployment of innovative big data and AI applications.
Ensure Best Practices:Guarantee that Databricks best practices are applied throughout all projects to maintain high-quality service and successful implementation.
Support Project Management:Assist the Professional Services leader and project managers with estimating efforts and managing risks within customer proposals and statements of work.
Architect Complex Solutions:Design, develop, deploy, and document complex customer engagements, either independently or as part of a technical team, serving as the technical lead and authority.
Enable Knowledge Transfer:Facilitate the transfer of knowledge and provide training to team members, customers, and partners, including the creation of reusable project documentation.
Contribute to Consulting Excellence:Share expertise with the consulting team and offer best practices for client engagement, enhancing the effectiveness and efficiency of other teams.

Minimum qualifications:

Educational Background:Bachelor’s degree in Computer Science, Information Technology, or a related field (or equivalent experience).
Experience:
3+ years of experience as a Data Engineer, with proficiency in at least two major cloud platforms (AWS, Azure, GCP).
Proven experience in designing, developing, and implementing comprehensive data engineering solutions using Databricks, specifically for large-scale data processing and integration projects
Develop scalable streaming and batch solutions using cloud-native components.
Perform data transformation tasks, including cleansing, aggregation, enrichment, and normalisation, utilising Databricks and related technologies.
Experience in applying DataOps principles and implementing CI/CD and DevOps practices within data environments to optimize development and deployment workflows.
Technical Skills:
Expert-level proficiency in Spark Scala, Python, and PySpark.
In-depth knowledge of data architecture, including Spark Streaming, Spark Core, Spark SQL, and data modeling.
Hands-on experience with various data management technologies and tools, such as Kafka, StreamSets, and MapReduce.
Proficient in using advanced analytics and machine learning frameworks, including Apache Spark MLlib, TensorFlow, and PyTorch, to drive data insights and solutions.
Databricks Specific Skills:
Extensive experience in data migration from on-premises to cloud environments and in implementing data solutions on Databricks across cloud platforms (AWS, Azure, GCP).
Skilled in designing and executing end-to-end data engineering solutions using Databricks, focusing on large-scale data processing and integration.
Proven hands-on experience with Databricks administration and operations, including notebooks, clusters, jobs, and data pipelines.
Experience integrating Databricks with other data tools and platforms to enhance overall data management and analytics capabilities.
Good to have Certifications:
Certification in Databricks Engineering (Professional)
Microsoft Certified: Azure Data Engineer Associate
GCP Certified: Professional Google Cloud Certified.
AWS Certified Solutions Architect Professional

Details

3+ years of experience as a Data Engineer, with proficiency in at least two major cloud platforms (AWS, Azure, GCP).
Proven experience in designing, developing, and implementing comprehensive data engineering solutions using Databricks, specifically for large-scale data processing and integration projects
Develop scalable streaming and batch solutions using cloud-native components.
Perform data transformation tasks, including cleansing, aggregation, enrichment, and normalisation, utilising Databricks and related technologies.
Experience in applying DataOps principles and implementing CI/CD and DevOps practices within data environments to optimize development and deployment workflows.
Expert-level proficiency in Spark Scala, Python, and PySpark.
In-depth knowledge of data architecture, including Spark Streaming, Spark Core, Spark SQL, and data modeling.
Hands-on experience with various data management technologies and tools, such as Kafka, StreamSets, and MapReduce.
Proficient in using advanced analytics and machine learning frameworks, including Apache Spark MLlib, TensorFlow, and PyTorch, to drive data insights and solutions.
Extensive experience in data migration from on-premises to cloud environments and in implementing data solutions on Databricks across cloud platforms (AWS, Azure, GCP).
Skilled in designing and executing end-to-end data engineering solutions using Databricks, focusing on large-scale data processing and integration.
Proven hands-on experience with Databricks administration and operations, including notebooks, clusters, jobs, and data pipelines.
Experience integrating Databricks with other data tools and platforms to enhance overall data management and analytics capabilities.
Certification in Databricks Engineering (Professional)
Microsoft Certified: Azure Data Engineer Associate
GCP Certified: Professional Google Cloud Certified.
AWS Certified Solutions Architect Professional