What you'll do:

• Design and implement scalable and efficient data architectures to support the organization's data processing needs
• Work closely with cross-functional teams to understand data requirements and ensure that data solutions align with business objectives
• Oversee the development of robust ETL processes to extract, transform, and load data from various sources into the data warehouse
• Ensure data quality and integrity throughout the ETL process, implementing best practices for data cleansing and validation
• Stay abreast of emerging trends and technologies in big data and analytics, and assess their applicability to the organization's data strategy
• Implement and optimize big data technologies to process and analyze large datasets efficiently
• Collaborate with the IT infrastructure team to integrate data engineering solutions with cloud platforms, ensuring scalability, security, and performance
• Performance Monitoring and Optimization:
• Implement monitoring tools and processes to track the performance of data pipelines and proactively address any issues
• Optimize data processing workflows for improved efficiency and resource utilization
• Maintain comprehensive documentation for data engineering processes, data models, and system architecture
• Ensure that team members follow documentation standards and best practices.
• Collaboration and Communication:
• Collaborate with data scientists, analysts, and other stakeholders to understand their data needs and deliver solutions that meet those requirements
• Communicate effectively with technical and non-technical stakeholders, providing updates on project status, challenges, and opportunities.

What makes you a great fit:

• 6-8 years of professional experience in data engineering
  
• In-depth knowledge of data modeling, ETL processes, and data warehousing
• In-depth knowledge of building the data warehouse using Snowflake
• Should have experience in data ingestion, data lakes, data mesh and data governance
• Must have experience in Python programming
• Strong understanding of big data technologies and frameworks, such as Hadoop, Spark, and Kafka
• Experience with cloud platforms, such as AWS, Azure, or Google Cloud
• Familiarity with database systems like SQL, NoSQL, and data pipeline orchestration tools
• Excellent problem-solving and analytical skills
• Strong communication and interpersonal skills
• Proven ability to work collaboratively in a fast-paced, dynamic environment