Data Engineering Lead

Job Overview: As the Data Engineering Lead, you will be at the forefront of shaping and executing our data engineering strategies. You will lead a team of skilled engineers, collaborate across departments, and leverage your proficiency in Python, AWS, Apache Flink/ Apache Spark, Scala, and IoT to architect and deliver high-performance data solutions that drive insights and innovation.

Responsibilities:

1. Leadership and Team Management:
   • Lead and mentor a team of engineers, fostering a collaborative and innovative work environment.
   • Define team goals, allocate tasks, and ensure the successful and timely completion of projects.
   • Provide technical guidance, coaching, and professional development to team members.
2. Data Pipeline Architecture and Development:
   • Architect, design, and implement scalable and robust data pipelines for collecting, processing, and transforming data from diverse sources, including IoT devices.
   • Optimize data pipelines for performance, reliability, and scalability.
   • Collaborate with data scientists and analysts to ensure data availability and accuracy.
3. Python, Scala, and ETL Development:
   • Utilize your strong Python and Scala programming skills to develop and maintain ETL processes, data integration, and data transformation workflows.
   • Create and enhance data processing scripts and programs to handle large datasets.
4. AWS and Cloud Infrastructure:
   • Leverage AWS services such as Lambda, Glue, Kinesis, Managed Kafka, S3, and Athena to design and deploy cloud-based data solutions.
   • Design, implement, and manage data storage, processing, and analytics solutions in AWS.

1. Apache Flink/Apache Spark Expertise:
   • Utilize your expertise in Apache Flink/ Apache Spark to develop real-time data processing pipelines, ensuring low-latency and high-throughput data streaming.
2. IoT Integration:
   • Collaborate closely with IoT teams to seamlessly integrate data from IoT devices into data pipelines and processing workflows.
   • Ensure secure handling and processing of IoT data streams.
3. Data Quality and Governance:
   • Implement rigorous data quality checks, validation processes, and data governance best practices.
   • Continuously monitor and maintain data pipelines to ensure data accuracy and consistency.
4. Documentation and Communication:
   • Thoroughly document data engineering processes, pipeline architectures, and technical specifications.
   • Effectively communicate complex technical concepts to both technical and non-technical stakeholders.

Qualifications:

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• Proven experience (9+ years) in data engineering, ETL development, and data integration.
• Strong proficiency in Python and Scala programming languages.
• Complementary skills - BigData, DataLake , DataMesh
• In-depth knowledge of AWS services and cloud architecture for data engineering.
• Expertise in Apache Flink/Apache Spark for real-time data processing.
• Experience with IoT data processing and integration is highly desirable.
• Solid grasp of data modelling, database design, and SQL.
• Outstanding leadership and team management skills.
• Strong problem-solving capabilities and a proactive approach to technical challenges.
• Excellent verbal and written communication skills.
• Nice to Have – Experience in Docker, Kubernetes

Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

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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