Senior Data Engineer

Automat-it is where high-growth startups turn when they need to move faster, scale smarter, and make the most of the cloud. As an AWS Premier Partner and Strategic Partner, we deliver hands-on DevOps, FinOps, and GenAI support that drives real results.

We work across EMEA and the US, fueling innovation and solving complex challenges daily. Join us to grow your skills, shape bold ideas, and help build the future of tech.

We’re looking for a **Senior Data Engineer** to play a key role in building our Data & Analytics practice and delivering modern data solutions on AWS for our clients. In this role, you'll be a customer-facing, hands-on technical engineer who designs and implements end-to-end data pipelines and analytics platforms using AWS services like AWS Glue, Amazon OpenSearch Service, Amazon Redshift, and Amazon QuickSight. From migrating legacy ETL workflows to AWS Glue to building scalable data lakes for AI/ML training, you'll ensure our customers can unlock the full value of their data. You’ll work closely with client stakeholders (from startup founders and CTOs to data engineers) to create secure, cost-efficient architectures that drive real business impact.

Work location - hybrid from Madrid

If you are interested in this opportunity, please submit your CV in English.

**Requirements**:

- **5+ years of experience** in data engineering, data analytics, or a related field, including **3+ years of hands-on AWS experience** (designing, building, and maintaining data solutions on AWS).
- **Production experience with AWS cloud and data services**, including building solutions at scale with tools like AWS Glue, Amazon Redshift, Amazon S3, Amazon Kinesis, Amazon OpenSearch Service, etc.
- **Skilled in AWS analytics and dashboards tools** - hands-on expertise with services such as Amazon QuickSight or other BI tools (Tableau, Power BI) and Amazon Athena.
- **Experience with ETL pipelines** - ability to build ETL/ELT workflows (using AWS Glue, Spark, Python, SQL).
- **Experience with data warehousing and data lakes -**ability to design and optimize data lakes (on S3), Amazon Redshift for data warehousing, and Amazon OpenSearch for log/search analytics.
- **Proficiency in programming** (Python/PySpark) and **SQL** skills for data processing and analysis.
- **Understanding of cloud security and data governance** best practices (encryption, IAM, data privacy).
- **Excellent communication skills** with an ability to explain complex data concepts in clear terms. Comfortable working directly with clients and guiding technical discussions.
- **Proven ability to lead end-to-end technical engagements** and work effectively in fast-paced, Agile environments.
- **AWS certification** - AWS certifications, especially in Data Analytics or Machine Learning are a plus.
- **DevOps/MLOps knowledge** - experience with Infrastructure as Code (Terraform), CI/CD pipelines, containerization, and AWS AI/ML services (SageMaker, Bedrock) is a plus.

**Responsibilities**:

- Design, develop, and deploy AWS-based data and analytics solutions to meet customer requirements. Ensure architectures are highly available, scalable, and cost-efficient.
- Develop dashboards and analytics reports using Amazon QuickSight or equivalent BI tools.
- Migrate and modernize existing data workflows to AWS. Re-architect legacy ETL pipelines to AWS Glue and move on-premises data systems to Amazon OpenSearch/Redshift for improved scalability and insights.
- Build and manage multi-modal data lakes and data warehouses for analytics and AI. Integrate structured and unstructured data on AWS (e.g. S3, Redshift) to enable advanced analytics and generative AI model training using tools like SageMaker.
- Implement infrastructure automation and CI/CD for data projects. Use Infrastructure as Code (Terraform) and DevOps best practices to provision AWS resources and continuously integrate/deploy data pipeline code.
- Lead customer workshops and proof-of-concepts (POCs) to demonstrate proposed solutions. Run technical sessions (architecture whiteboards, Well-Architected reviews) to validate designs and accelerate customer adoption.
- Collaborate with engineering teams (Data Scientist, DevOps and MLOps teams) and stakeholders to deliver projects successfully. Ensure solutions follow AWS best practices and security guidelines, and guide client teams in implementing according to the plan.
- Stay up-to-date on emerging data technologies and mentor team members. Continuously learn new AWS services (e.g. AWS Bedrock, Lake Formation) and industry trends, and share knowledge to improve our delivery as we grow the Data & Analytics practice.

**Benefits**:

- Professional training and certifications covered by the company (AWS, FinOps, Kubernetes, etc.)
- International work environment
- Referral program - enjoy cooperation with your colleagues and get a bonus
- Company events and social gatherings (happy hours, team events, knowledge sharing, etc.)
- English classes
- Soft s