Koantek
Data Engineer- Multiple Positions
Remote Data Engineering role with clear candidate location fit.
PostedJul 19, 2026
Eligible countries1 accepted country
Seniority signalMiddle
Work settingRemote
Accepted candidate locations
USA
Role overview
Data Engineer- Multiple Positions
Requirements and responsibilities
Readable role content extracted into sections for faster review.
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
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