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.\n\nKey Responsibilities\n- Lead the design, development, and optimization of large-scale data pipelines and infrastructures using technologies like Apache Airflow, Spark, Kafka, and more.\n- 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).\n- Develop and maintain real-time data processing solutions for high-volume data collection operations using technologies like Spark Streaming and Kafka.\n- Optimize data storage strategies using technologies such as Amazon S3, HDFS, and Parquet/Avro file formats for efficient querying and cost management.\n- Build and maintain high-quality ETL pipelines, ensuring robust data collection and transformation processes with a focus on scalability and fault tolerance.\n- 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.\n- Mentor junior engineers (SDE-I) and foster a collaborative, growth-oriented environment.\n- Participate in technical discussions, contributing to architectural decisions, and proactively identifying improvements for scalability, performance, and cost-efficiency.\n- 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.\n- Implement effective data partitioning strategies and indexing for performance optimization in distributed databases such as DynamoDB, Cassandra, or HBase.\n- Stay current with advancements in data engineering, orchestration tools, and emerging cloud technologies, continually enhancing the platform’s capabilities\n\nQualifications & Experience:\n- 4-5+ years of hands-on experience with Apache Airflow and other orchestration tools for managing large-scale workflows and data pipelines.\n- Expertise in AWS technologies, Athena, AWS Glue, DynamoDB, Apache Spark, PySpark, SQL, and NoSQL databases.\n- 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.\n- Proficiency in web crawling frameworks, including Node.js, HTTP protocols, Puppeteer, Playwright, and Chromium for large-scale data extraction.\n- Experience with monitoring and observability tools such as Grafana, Prometheus, Elasticsearch, and familiarity with monitoring and optimizing resource utilization in distributed systems.\n- Strong understanding of infrastructure as code using Terraform, automated CI/CD pipelines with Jenkins, and event-driven architecture with Kafka.\n- Experience with data lake architectures and optimizing storage using formats such as Parquet, Avro, or ORC.\n- Strong background in optimizing query performance and data processing frameworks (Spark, Flink, or Hadoop) for efficient data processing at scale.\n- Knowledge of containerization (Docker, Kubernetes) and orchestration for distributed system deployments.\n- Deep experience in designing resilient data systems with a focus on fault tolerance, data replication, and disaster recovery strategies in distributed environments.\n- Strong data engineering skills, including ETL pipeline development, stream processing, and distributed systems.\n- Excellent problem-solving abilities, with a collaborative mindset and strong communication skills.