Senior AWS Cloud Engineer

Osprey Life Sciences, LLC is a leading consulting and services firm specializing in providing comprehensive technology solutions for Life Sciences IT organizations. Our primary focus is on assisting these organizations in effectively and efficiently delivering solutions that support their business objectives and their mission to enhance human health and improve lives. Position Summary Osprey is seeking a highly skilled Senior AWS Cloud Data Engineer to design, build, and support scalable, secure, and AI-ready data platforms on Amazon Web Services (AWS). This role will focus on establishing the data engineering foundation that enables advanced analytics, machine learning, and generative AI initiatives. The ideal candidate will possess deep expertise in AWS data services, cloud-native data architecture, modern data engineering practices, and AI-enablement technologies. This individual will collaborate closely with data scientists, AI/ML engineers, bioinformatics teams, and business stakeholders to deliver high-quality data solutions that support enterprise-scale analytics and AI workloads. Key Responsibilities Design, develop, and maintain scalable batch and streaming data pipelines on AWS. Build and support cloud-native data lakes and analytics platforms utilizing Amazon S3, AWS Glue, Athena, Redshift, and Lake Formation. Implement metadata management, data quality frameworks, schema evolution, and lineage capabilities. Optimize data platform performance, reliability, scalability, and cost efficiency. Build and maintain machine learning-ready datasets, feature pipelines, and training/inference data workflows. Support AI and Generative AI initiatives, including embedding pipelines, vector search, and retrieval-augmented generation (RAG) architectures. Prepare, index, and refresh source data required for AI knowledge retrieval and semantic search. Collaborate with AI/ML engineers and bioinformatics teams to support AWS SageMaker and Amazon Bedrock solutions. AWS Services & Integration Design and implement integrations leveraging AWS services including: AWS Glue Amazon EMR AWS Lambda AWS Step Functions Amazon Kinesis / MSK AWS Database Migration Service (DMS) Amazon SageMaker Amazon Bedrock Design event-driven and serverless data architectures. Support ingestion and processing of structured, semi-structured, and unstructured data sources. Security, Governance & Reliability Implement security best practices including IAM, KMS, encryption, and least-privilege access controls. Support data governance initiatives including auditability, PII protection, and access segregation. Build fault-tolerant, observable, and recoverable data pipelines. Partner with security, compliance, and governance teams to ensure adherence to organizational standards. Develop infrastructure using Infrastructure as Code (Terraform, AWS CDK, or CloudFormation). Implement CI/CD pipelines and Git-based development workflows. Apply software engineering best practices including automated testing, documentation, and version control. Monitor platform health, performance, and cloud spending while driving continuous improvement. Required Qualifications Strong hands-on experience with Amazon S3, AWS Glue, Athena, Redshift, and Lake Formation. Advanced proficiency in Python, including Pandas, PySpark, and Boto3. Strong SQL development and performance tuning experience. Experience building Spark-based ETL solutions using AWS Glue or Amazon EMR. Experience designing dimensional, analytical, and reporting-focused data models. Experience supporting machine learning and AI workloads through data engineering and data platform design. Familiarity with AWS AI services such as SageMaker, Bedrock, Comprehend, Rekognition, or Textract. Experience working with vector databases, embeddings, GraphDB technologies, or OpenSearch vector capabilities. Understanding of modern Generative AI data patterns including RAG architectures, embeddings, and unstructured data preparation. Experience with Infrastructure as Code using Terraform, CloudFormation, or AWS CDK. Experience implementing CI/CD pipelines using GitHub Actions or similar tools. Knowledge of cloud monitoring, logging, observability, and cost optimization practices. Strong understanding of distributed systems and cloud architecture principles. Preferred Qualifications AWS Certified Data Engineer – Associate or equivalent AWS certification. Experience working within regulated environments such as Life Sciences, Healthcare, Biotech, or Pharmaceutical organizations. Exposure to feature stores, semantic layers, knowledge graphs, or advanced data discovery platforms. Strong communication and collaboration skills with technical and non-technical stakeholders. Experience partnering with AI/ML teams to enable production AI solutions. What Success Looks Like Reliable, scalable, and secure data pipelines that support enterprise analytics and AI initiatives. High-quality, trusted, and governed datasets used across the organization. Accelerated AI experimentation and model development through well-designed data foundations. Cost-effective AWS data platforms that support long-term growth and innovation. Strong collaboration across data, AI, engineering, and business teams to deliver measurable business value. We offer an excellent compensation and benefits package with challenge and opportunity to learn, grow and contribute to a stimulating, fast-paced environment. Osprey is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, gender, national origin, disability status, protected veteran status or any other characteristic protected by law. #J-18808-Ljbffr