Data Lead Engineer – Snowflake

Data Lead Engineer – Snowflake Remote Contract Brazil/ MexicoResponsibilities • 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