Data Engineer II

Job Summary: Building on the foundation of the SDE-I role, the DE- II position takes on a greater level of responsibility and leadership. You'll play a crucial role in driving the evolution and efficiency of our data collection and analytics platform, capable of handling terabyte-scale data and billions of data points. Key Responsibilities Lead the design, development, and optimization of large-scale data pipelines and infrastructures using technologies like Apache Airflow, Spark, Kafka, and more. Architect and implement distributed data processing solutions to handle terabyte-scale datasets and billions of records efficiently across multi-region cloud infrastructure (AWS, GCP, DO). Develop and maintain real-time data processing solutions for high-volume data collection operations using technologies like Spark Streaming and Kafka. Optimize data storage strategies using technologies such as Amazon S3, HDFS, and Parquet/Avro file formats for efficient querying and cost management. Build and maintain high-quality ETL pipelines, ensuring robust data collection and transformation processes with a focus on scalability and fault tolerance. Collaborate with data analysts, researchers, and cross-functional teams to define and maintain data quality metrics, implement robust data validation, and enforce security best practices. Mentor junior engineers (SDE-I) and foster a collaborative, growth-oriented environment. Participate in technical discussions, contributing to architectural decisions, and proactively identifying improvements for scalability, performance, and cost-efficiency. Ensure application performance monitoring (APM) is in place, utilizing tools like Datadog, New Relic, or similar to proactively monitor and optimize system performance, detect bottlenecks, and ensure system health. Implement effective data partitioning strategies and indexing for performance optimization in distributed databases such as DynamoDB, Cassandra, or HBase. Stay current with advancements in data engineering, orchestration tools, and emerging cloud technologies, continually enhancing the platform’s capabilities Qualifications & Experience: 4-5+ years of hands-on experience with Apache Airflow and other orchestration tools for managing large-scale workflows and data pipelines. Expertise in AWS technologies, Athena, AWS Glue, DynamoDB, Apache Spark, PySpark, SQL, and NoSQL databases. Experience in designing and managing distributed data processing systems that scale to terabyte and billion-scale datasets using cloud platforms like AWS, GCP, or Digital Ocean. Proficiency in web crawling frameworks, including Node.js, HTTP protocols, Puppeteer, Playwright, and Chromium for large-scale data extraction. Experience with monitoring and observability tools such as Grafana, Prometheus, Elasticsearch, and familiarity with monitoring and optimizing resource utilization in distributed systems. Strong understanding of infrastructure as code using Terraform, automated CI/CD pipelines with Jenkins, and event-driven architecture with Kafka. Experience with data lake architectures and optimizing storage using formats such as Parquet, Avro, or ORC. Strong background in optimizing query performance and data processing frameworks (Spark, Flink, or Hadoop) for efficient data processing at scale. Knowledge of containerization (Docker, Kubernetes) and orchestration for distributed system deployments. Deep experience in designing resilient data systems with a focus on fault tolerance, data replication, and disaster recovery strategies in distributed environments. Strong data engineering skills, including ETL pipeline development, stream processing, and distributed systems. Excellent problem-solving abilities, with a collaborative mindset and strong communication skills.