Data Platform Engineer

January 19

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Tiger Analytics

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Tiger Analytics is a leading AI and analytics consulting firm that specializes in leveraging data science and machine learning to provide strategic business insights across various industries. They offer services in data strategy, AI engineering, and business intelligence to enable data-driven decision-making and digital transformation for their clients. Tiger Analytics collaborates with top technology partners like Microsoft, Google Cloud, and AWS to deliver cutting-edge solutions. They serve a diverse range of sectors including consumer packaged goods, healthcare, and finance, helping businesses operationalize insights and differentiate with AI and machine learning technologies.

Machine Learning • Predictive Analytics • Forecasting • Optimization • Natural Language Processing

1001 - 5000 employees

Founded 2011

🤖 Artificial Intelligence

🤝 B2B

📋 Description

• Tiger Analytics is a fast-growing advanced analytics consulting firm. • Our consultants bring deep expertise in Data Science, Machine Learning and AI. • We are the trusted analytics partner for several Fortune 100 companies, enabling them to generate business value from data. • Our business value and leadership have been recognized by various market research firms, including Forrester and Gartner. • We are looking for top-notch talent as we continue to build the best analytics global consulting team in the world.

🎯 Requirements

• Lead the solution design and implementation of end-to-end data pipelines that support real-time streaming, data syndication, and integration across multiple platforms, focusing on high availability, scalability, and performance. • Manage and mentor a team of engineers, providing guidance on technical approaches, troubleshooting, and problem resolution. • Lead the implementation of best practices for coding, testing, deployment, and monitoring of data products. • Drive modernization efforts for existing data platforms, ensuring smooth migration and integration with cloud-native technologies and streaming data frameworks. • Design and implement robust streaming data pipelines using technologies such as Apache Kafka, Kinesis, Flume, and other relevant tools to enable real-time data processing and integration. • Intimately understand AWS to architect and deploy cloud-based data solutions that integrate seamlessly with the larger data ecosystem. • Identify bottlenecks in data workflows and implement solutions to improve performance, reduce latency, and increase throughput. • Ensure that the data platform is reliable, resilient, and able to handle high-volume data processing workloads, with a strong focus on monitoring, incident management, and uptime. • Advise and enforce best practices around data management processes, batch vs. real-time processing, data storage strategies, and data quality. • Work closely with cross-functional teams, including data scientists, product managers, business analysts, and IT operations, to deliver business value and ensure alignment of architecture with organizational goals. • Proven experience designing and implementing scalable, high-performance data systems in cloud environments, specifically for large-scale data ingestion, storage, and processing. • Advanced knowledge of real-time data streaming platforms such as Apache Kafka and AWS Kinesis. • Expertise in working with AWS as well as a deep understanding of the intricacies involved in moving cloud workloads to on-prem. • Proficiency in Databricks for big data processing, particularly in managing data pipelines and large-scale analytics workloads. • Strong hands-on experience with Scala and Java for developing data-intensive applications, data pipelines, and stream processing systems. • Familiarity with Apache Spark for distributed data processing. • Experience with Kubernetes or other orchestration frameworks. • Experience writing infrastructure as code.

🏖️ Benefits

• Significant career development opportunities exist as the company grows. • The position offers a unique opportunity to be part of a small, challenging, and entrepreneurial environment, with a high degree of individual responsibility.