Senior Data AWS Engineer @
2 weeks ago
- 5+ years of experience in data engineering on AWS
- Strong knowledge of AWS data stack: Glue, Athena, Lake Formation, S3, Step Functions, Lambda, RDS etc.
- Proficient in Python for scripting, automation and data manipulation tasks
- Experience with PySpark for building scalable, distributed ETL/ELT pipelines using Apache Spark (especially within AWS Glue)
- Experience designing enterprise data lakes and data mesh architecture
- Experience in structuring and modelling data in both relational and non-relational forms
- Ability to elaborate and propose relational/non-relational approach
- Advanced knowledge of databases, including query optimization, relational and non-relational schema design
- Expertise in data storage technologies, including files, relational databases, NoSQL and various data types (structured, unstructured, metrics, logs)
- Deep understanding of data governance, lineage and metadata management
- Experience in implementing data quality frameworks, validation checks and monitoring to ensure reliability of data
- Hands-on experience with real-time data streaming technologies and patterns (e.g. Kinesis, Kafka, or similar) for event-driven architectures
- Solid awareness of DevSecOps practices, including automation of CI/CD pipelines (GitHub Actions or similar), infrastructure as code (Terraform), IAM roles, encryption, SAST/SCA tools integration, auditability
- Leadership and team coordination skills - ability to lead distributed teams
- AWS Certified Data Engineer - Associate, additional certifications such as AWS Certified DevOps Engineer, AWS Certified Solutions Architect or AWS Certified Developer are a plus
- Fluent in English (written and spoken)
- AWS Certified Data Engineer - Associate, additional certifications such as AWS Certified DevOps Engineer, AWS Certified Solutions Architect or AWS Certified Developer are a plus
- Leadership and team coordination skills - ability to lead distributed teams
- Solid awareness of DevSecOps practices, including automation of CI/CD pipelines (GitHub Actions or similar), infrastructure as code (Terraform), IAM roles, encryption, SAST/SCA tools integration, auditability
- Hands-on experience with real-time data streaming technologies and patterns (e.g. Kinesis, Kafka, or similar) for event-driven architectures
- Experience in implementing data quality frameworks, validation checks and monitoring to ensure reliability of data
- Deep understanding of data governance, lineage and metadata management
- Expertise in data storage technologies, including files, relational databases, NoSQL and various data types (structured, unstructured, metrics, logs)
- Advanced knowledge of databases, including query optimization, relational and non-relational schema design
- Ability to elaborate and propose relational/non-relational approach
- Experience in structuring and modelling data in both relational and non-relational forms
- Experience designing enterprise data lakes and data mesh architecture
- Experience with PySpark for building scalable, distributed ETL/ELT pipelines using Apache Spark (especially within AWS Glue)
- Proficient in Python for scripting, automation and data manipulation tasks
- Strong knowledge of AWS data stack: Glue, Athena, Lake Formation, S3, Step Functions, Lambda, RDS etc.
For Digital Hub Warsaw, we are looking for:
Senior Data AWS Engineer
Participate in a global, strategic project in the Bayer Consumer Health division that will influence data in our business and manufacturing processes going forward. Our AWS-based data platform aggregates information from various sources to promote efficiency, creativity and better decision-making. To build scalable, high-performing and secure pipelines while working with teams around the world, we are seeking a hands-on Data Engineer. Contribute to a platform that will shape Bayer's digital future for Consumer Health division.
,[Play a key role in driving the design and implementation of AWS Data Lake, including orchestration and security guardrails, Define and enforce data architecture standards, including metadata, lineage and governance, Build and optimize ETL/ELT pipelines using AWS Glue, Spark, Athena, Lake Formation, S3, Step Functions, Lambda, Collaborate with DevSecOps engineers on infrastructure automation and CI/CD pipelines using tools such as GitHub Actions and Terraform, Automate deployment and management of data infrastructure and workflows to ensure reliability and scalability, Implement and manage IAM policies, KMS encryption, VPC configurations and audit mechanisms to ensure security and compliance, Ensure platform scalability, performance and cost efficiency (e.g., partitioning, catching strategies), Coordinate integration with AI/ML pipelines, including feature engineering and data validation, Participate in validation and documentation efforts aligned with GxP, Engage with stakeholders (Product, PM, PO, Architects) to align technical design with business needs, Manage and reduce technical debt in data pipelines and infrastructure, Act as technical lead and reviewer for offshore data engineers] Requirements: AWS, Data engineering, Glue, Athena, AWS S3, AWS Lambda, Amazon RDS, Python, PySpark, ETL, Apache Spark, Data Lake, Infrastructure as Code, DevOps, GitHub, NoSQL, CD pipeline, Kafka Tools: GitHub. Additionally: Sport subscription, Training budget, Private healthcare, Lunch card, International projects, Free coffee, Canteen, Playroom, Free snacks, Free beverages, Free parking, In-house trainings, Modern office.-
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