Data Lead Engineer – Snowflake

Data Lead Engineer – Snowflake Remote Contract Brazil / Mexico

Responsibilities

• Technical Leadership : Provide technical direction and mentorship to a team of data engineers, ensuring best practices in coding, architecture, and data operations.
• End-to-End Ownership : Architect, implement, and optimize end-to-end data pipelines that process and transform large-scale datasets efficiently and reliably.
• Orchestration and Automation : Design scalable workflows using orchestration tools such as Apache Airflow, ensuring high availability and fault tolerance.
• Data Warehouse and Lake Optimization : Lead the implementation and optimization of Snowflake and data lake technologies like Apache Iceberg for storage, query performance, and scalability.
• Real-Time and Batch Processing : Build robust systems leveraging Kafka, SQS, or similar messaging technologies for real-time and batch data processing.
• Cross-Functional Collaboration : Work closely with Data Science, Product, and Engineering teams to define data requirements and deliver actionable insights.
• Data Governance and Security : Establish and enforce data governance frameworks, ensuring compliance with regulatory standards and maintaining data integrity.
• Scalability and Performance : Develop strategies to optimize performance for systems processing terabytes of data daily while ensuring scalability.
• Team Building : Foster a collaborative team environment, driving skill development, career growth, and continuous learning within the team.
• Innovation and Continuous Improvement : Stay ahead of industry trends to evaluate and incorporate new tools, technologies, and methodologies into the organization. Qualifications Required Skills :
• 8+ years of experience in data engineering with a proven track record of leading data projects or teams.
• Strong programming skills in Python, with expertise in building and optimizing ETL pipelines.
• Extensive experience with Snowflake or equivalent data warehouses for designing schemas, optimizing queries, and managing large datasets.
• Expertise in orchestration tools like Apache Airflow, with experience in building and managing complex workflows.
• Deep understanding of messaging queues such as Kafka, AWS SQS, or similar technologies for real-time data ingestion and processing.
• Demonstrated ability to architect and implement scalable data solutions handling terabytes of data.
• Hands-on experience with Apache Iceberg for managing and optimizing data lakes.
• Proficiency in containerization and orchestration tools like Docker and Kubernetes for deploying and managing distributed systems.
• Strong understanding of CI / CD pipelines, including version control, deployment strategies, and automated testing.
• Proven experience working in an Agile development environment and managing cross-functional team interactions.
• Strong background in data modeling, data governance, and ensuring compliance with data security standards.
• Experience working with cloud platforms like AWS, Azure, or GCP. Preferred Skills :
• Proficiency in stream processing frameworks such as Apache Flink for real-time analytics.
• Familiarity with programming languages like Scala or Java for additional engineering tasks.
• Exposure to integrating data pipelines with machine learning workflows.
• Strong analytical skills to evaluate new technologies and tools for scalability and performance. Leadership Skills :
• Proven ability to lead and mentor data engineering teams, promoting collaboration and a culture of excellence.
• Exceptional communication and interpersonal skills to articulate complex technical concepts to stakeholders.
• Strategic thinking to align data engineering efforts with business goals and objectives. About Ampstek Ampstek is a global IT solutions partner serving clients across North America, Europe, APAC, LATAM, and MEA. We specialize in delivering talent and technology solutions for enterprise-level digital transformation, trading systems, data services, and regulatory compliance. Contact : Snehil Mishra Desk : Ext. 125 LinkedIn