Senior Data/Ml Engineer (Aws)

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data/ML Engineer (AWS) based in Spain.This role sits at the intersection of advanced data engineering and applied machine learning, focusing on building scalable, cloud-native data platforms on AWS. You will design and develop modern data lakes, streaming pipelines, and ML-driven services that power analytics and intelligent automation across enterprise systems. The position involves working with large-scale, multi-source datasets spanning operational, customer, and marketing domains. You will contribute to end-to-end data lifecycle design, from ingestion and transformation to model deployment and monitoring. The environment is highly collaborative, involving data engineers, ML engineers, DevOps, and architecture teams. This is an opportunity to shape production-grade data ecosystems that directly enable AI-driven business capabilities.Accountabilities:In this role, you will be responsible for designing and delivering scalable data and ML solutions that support enterprise-grade analytics and AI use cases.Design and implement multi-zone data lake architectures on AWS using S3, including raw, curated, and analytics-ready layers aligned with enterprise requirements.Build and maintain batch and real-time data pipelines using services such as AWS Glue, Kinesis, and Step Functions to integrate diverse data sources.Develop ETL workflows, data transformations, and metadata management frameworks using AWS Glue Data Catalog and related tools.Deploy and operationalize ML models using Amazon SageMaker for use cases such as prediction, scoring, and segmentation.Integrate generative AI capabilities using Amazon Bedrock to enable intelligent automation, personalization, and enrichment workflows.Support data migration initiatives from Azure to AWS, including schema mapping, validation, reconciliation, and performance optimization.Implement data governance, security, and access controls using AWS Lake Formation and ensure compliance with data standards.Collaborate with cross-functional teams to define architecture, maintain documentation, and ensure data quality across all pipelines and outputs.Requirements:The ideal candidate brings strong experience in cloud data engineering and applied machine learning within AWS environments.5+ years of experience in data engineering or ML engineering, with at least 2+ years working extensively on AWS.Strong proficiency in Python and SQL with hands-on experience in building scalable data pipelines.Deep knowledge of AWS services including S3, Glue, Athena, Kinesis, Lambda, and Step Functions.Experience with Amazon SageMaker for training, tuning, deploying, and monitoring ML models in production.Working knowledge of Amazon Bedrock or other generative AI frameworks for enterprise use cases.Experience designing and maintaining data lake architectures with strong governance and security models.Familiarity with Azure data platforms and cloud migration projects is highly desirable.Strong understanding of data modeling, feature engineering, and ML integration best practices.Excellent problem-solving, communication, and collaboration skills in Agile environments.Benefits:Competitive compensation package aligned with experience and expertiseFully remote work opportunityExposure to cutting-edge AWS data and AI/ML technologiesOpportunity to work on large-scale enterprise data transformation programsCollaborative and cross-functional global engineering environmentCareer growth in advanced data engineering and machine learning domainsFlexible work culture supporting autonomy and ownershipHow Jobgether works:We use anAI-powered matching processto ensure your application is reviewed quickly, objectively, and fairly against the role’s core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.We appreciate your interest and wish you the best!Why Apply Through Jobgether?Data Privacy Notice:By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.#LI-CL1We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

The ideal candidate brings strong experience in cloud data engineering and applied machine learning within AWS environments. 5+ years of experience in data engineering or ML engineering, with at least 2+ years working extensively on AWS. Strong proficiency in Python and SQL with hands-on experience in building scalable data pipelines. Deep knowledge of AWS services including S3, Glue, Athena, Kinesis, Lambda, and Step Functions. Experience with Amazon SageMaker for training, tuning, deploying, and monitoring ML models in production. Working knowledge of Amazon Bedrock or other generative AI frameworks for enterprise use cases. Experience designing and maintaining data lake architectures with strong governance and security models. Familiarity with Azure data platforms and cloud migration projects is highly desirable. Strong understanding of data modeling, feature engineering, and ML integration best practices. Excellent problem-solving, communication, and collaboration skills in Agile environments.

Competitive compensation package aligned with experience and expertise Fully remote work opportunity Exposure to cutting-edge AWS data and AI/ML technologies Opportunity to work on large-scale enterprise data transformation programs Collaborative and cross-functional global engineering environment Career growth in advanced data engineering and machine learning domains Flexible work culture supporting autonomy and ownership How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role’s core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.