AWS ETL Developer

AWS ETL Developer

Positions Open: 2 Client: Leading Life Sciences & Technology Company (Corporate/Enterprise, USA – EST) Employment Type: Full-time (100%) Location: Remote (EU-based candidates) Start Date: ASAP Engagement: Ongoing Language Requirement: English C1

Interview process: 1) CV review 2) Interview with Devspace manager 3) Interview with end client 

Number of interviews: 2

About the Client

Our client is a leading life sciences and technology company supplying analytical instruments, laboratory equipment, reagents, software, and services used in scientific research, healthcare, biotechnology, and pharmaceutical manufacturing worldwide. Their mission is to help customers make the world healthier, cleaner, and safer — supporting breakthroughs from life-sciences research and clinical diagnostics to complex analytical challenges and drug development.

Their software portfolio includes enterprise lab informatics platforms such as Laboratory Information Management Systems (LIMS), Electronic Lab Notebooks (ELN), and data management tools that help laboratories manage, analyze, and automate data and workflows.

Role Overview

We're looking for 2 AWS ETL Developers to build and maintain event-driven ETL pipelines and cloud data ingestion workflows supporting enterprise lab informatics products. This role requires strong hands-on AWS Glue/Lambda expertise, comfort working independently in a fast-paced environment, and close collaboration with business and data analysts to translate requirements into technical solutions.

Key skills:

Core AWS Stack: Glue, Lambda, S3, Step Functions, IAM, RDS, CloudWatch, CloudTrail

Programming: Python (preferred), PySpark

Data Architecture: Data Lakes, S3-based data lake patterns, Apache Parquet, Apache Iceberg

DevOps/IaC: GitHub Actions or Jenkins (CI/CD), Terraform or CloudFormation

**Emerging Focus:**RAG (Retrieval-Augmented Generation) & Agentic Workflows (working knowledge)

Soft SkillsIndependent ownership, high-pressure resilience, cross-functional collaboration

Experience Level: 5–8 years in Data Engineering / Data Lake development

Key Responsibilities

1. Implement robust ETL pipelines using AWS Glue, defining extraction methods, transformation logic, and load procedures across diverse data sources.
2. Assess application data requirements and build AWS Lambda-based solutions for efficient data integration, processing, and application support.
3. Orchestrate jobs using AWS Step Functions and Lambda.
4. Implement event-driven pipelines triggered by new CSV file arrivals in S3.
5. Implement Data Lakes with ingestion from disparate sources (relational databases, flat files, APIs, streaming data).
6. Support CI/CD using GitHub Actions or Jenkins.
7. Automate infrastructure using Terraform or CloudFormation.
8. Monitor pipeline execution using CloudWatch and CloudTrail.
9. Use AI tools smartly and responsibly to deliver in a fast-paced environment.
10. Work effectively under high-pressure conditions.
11. Work independently with minimal instructions, taking full ownership and accountability.
12. Collaborate with business analysts and data analysts to translate business requirements into technical requirements.

Must-Have Skills

Critical / Non-Negotiable

• 5–8 years of experience developing Data Lakes with ingestion from disparate sources (relational databases, flat files, APIs, streaming data)
• Strong experience with AWS Glue, S3, Lambda, Step Functions, IAM, RDS, CloudWatch, CloudTrail
• Proficiency in Python (preferred) and PySpark for efficient data processing
• Hands-on experience implementing ETL pipelines with AWS Glue (extraction, transformation, load logic)
• Experience with S3-based data lake patterns

Data Architecture & Modeling

• Design and development of Data Platforms and cloud data ingestion pipelines
• Data modeling experience for relational databases
• Experience with Apache Parquet and Apache Iceberg as table formats for analytical datasets

Infrastructure & Operations

• Ability to collaborate with the Infrastructure team for AWS service provisioning (databases, IAM roles)
• Ability to work with AWS support for issue resolution
• CI/CD experience using GitHub Actions or Jenkins
• Terraform or CloudFormation experience

AI/ML Awareness

• Knowledge of RAG (Retrieval-Augmented Generation) and Agentic Workflows for providing enterprise data context to LLMs

Governance

• Good understanding of data validation, error handling, and audit logging

Soft Skills

• Smart, responsible use of AI tools to accelerate delivery
• Comfortable working under high pressure
• Self-directed, ownership-driven work style
• Strong collaboration skills with business/data analysts to bridge business and technical requirements

Please note: Background and reference checks will be conducted as part of the selection process.