Data Engineer

Job Description

JOB SUMMARY

We are seeking a highly skilled and experienced AWS Data Engineer specializing in Data Technologies to join our dynamic team. As an AWS Data Engineer you will be responsible for designing, implementing, and maintaining scalable, secure, and high-performance data solutions on the AWS platform. You will work closely with cross-functional teams to understand business requirements and translate them into robust data pipelines and data objects.

JOB RESPONSIBILITIES

• Design, implement, and maintain data solutions on AWS, leveraging services such as Amazon S3, Redshift, Glue, EMR, Athena, AWS Lake formation , Snaplogic others.
• Collaborate with data engineers, data scientists, and business stakeholders to understand data requirements and develop scalable data flows and solution to cater to business needs.
• Implement data lakes, data warehouses, and data pipelines to support batch and real-time data processing.
• Ensure data security, compliance, and governance best practices are implemented across all data solutions.
• Optimize and tune AWS data services for performance, cost, and scalability.
• Pro-actively shares best practice, ideas and insights across the team and utilizes the Agile Chapter structure to constructively inspire change towards a true DevOps operating model.
• Excellent on Translating Business Requirements into clear Stories, map the personas, deliver the data required as a MIO and challenge the business requirements.
• Being able to select and steer a DevOps team to ensure all related activities (BA, ETL, Data engineering, Visualization) are efficiently executed within the team towards a common goal – sprint planning.
• A mix of business focus and engineering mindset. Able to understand practical business problems and work alongside engineers to deliver compelling technical solutions.
• Communicate with impact and gains commitment of individuals and stakeholders in a compelling way.
• Relationship building, collaboration and influencing will be key given data complexity, challenging data integration needs and dealing with both central and decentralized business stakeholders.

TECHNICAL SKILLS

• Hands on working in AWS Cloud Services
• Proven experience designing and implementing data solutions on AWS, with a strong focus on data lakes, data warehousing, and ETL processes.
• Proficiency in programming languages such as Python, SQL ,PySpark, etc
• AWS ELT environment, S3, Glue, RedShift, Lambda etc.
• Proficiency in handling automated deployments using git(CI/CD), Jenkins etc.
• SQL, NoSQL and RDBMS databases
• Data modeling and ELT / ETL
• Understanding of Data analytics and visualization
• In-depth knowledge of AWS data services and their optimal usage in different scenarios.
• Strong understanding of data security, compliance, and governance principles.
• Excellent communication skills and the ability to collaborate effectively with cross-functional teams.

Qualifications

EXPERIENCE

• Overall 7+ years of Data Engineering , ETL , Data Management, Databases etc.
• 5+ year Hands-on experience with large scale cloud-based data technologies such as AWS ELT environment, S3, Glue, RedShift, Lambda, Python/ PySpark etc.
• Knowledge of Snaplogic, Data modeling and architecture concepts, analytics, data science, service management is preferrable.

PREFERRED QUALIFICATIONS

• Bachelor's or Master's degree in Computer Science, Engineering, or related field.
• Certification in AWS Solution Architect or Developer or Data Analytics – highly preferred.
• Experience with AWS Cloud – Must have.
• Data Management, Governance, Data modelling, ETL    - nice to have.
• BI Tools understanding – nice to have.
• Agile – must have

Additional Information

Note: Syngenta is an Equal Opportunity Employer and does not discriminate in recruitment, hiring, training, promotion or any other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, gender identity, marital or veteran status, disability, or any other legally protected status.

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