Big Data Engineer

Big Data EngineerMandatory skills: Apache Spark/ Hive/ Kafka/ Amazon Glue/ Google Dataflow/ Talend MDM/ Hadoop/ Presto/ Strong experience with MySQL, PostgreSQL, MongoDB, Cassandra.Role: Big Data EngineerJob Overview: We're seeking a highly skilled Data Engineer, Big Data Engineer to build scalable data pipelines, develop ML models, and integrate big data systems. You'll work with structured, semi-structured, and unstructured data, focusing on optimizing data systems, building ETL pipelines, and deploying AI models in cloud environments.Key Responsibilities:Data Ingestion: Build scalable ETL pipelines using Apache Spark, Talend, AWS Glue, Google Dataflow, Apache NiFi. Ingest data from APIs, file systems, and databases.Data TransformationValidation: Use Pandas, Apache Beam, and Dask for data cleaning, transformation, and validation. Automate data quality checks with Pytest, Unittest.Big Data Systems: Process large datasets with Hadoop, Kafka, Apache Flink, Apache Hive. Stream real-time data using Kafka, Google Cloud PubSub.Task Queues: Manage asynchronous processing with Celery, RQ, RabbitMQ, or Kafka. Implement retry mechanisms and track task status.Scalability: Optimize for performance with distributed processing (Spark, Flink), parallelization (joblib), and data partitioning.CloudStorage: Work with AWS, Azure, GCP, Databricks. Store and manage data with S3, BigQuery, Redshift, Synapse Analytics, and HDFS.Required Skills:ETL Data Processing: Expertise in Apache Spark, AWS Glue, Google Dataflow, Talend.Big Data Tools: Proficient with Hadoop, Kafka, Apache Flink, Hive, Presto.Databases: Strong experience with MySQL, PostgreSQL, MongoDB, Cassandra.Machine Learning: Hands-on with TensorFlow, PyTorch, Scikit-learn, XGBoost.Cloud Platforms: Experience with AWS, Azure, GCP, Databricks.Task Management: Familiar with Celery, RQ, RabbitMQ, Kafka.Version Control: Git for source code management.Desirable Skills:Real-time Data Processing: Experience with Apache Pulsar, Google Cloud PubSub.Data Warehousing: Familiarity with Redshift, BigQuery, Synapse Analytics.Scalability Optimization: Knowledge of load balancing (NGINX, HAProxy) and parallel processing.Data Governance: Use of MLflow, DVC, or other tools for model and data versioning.Tools Technologies: ETL: Apache Spark, Talend, AWS Glue, Google Dataflow. Big Data: Hadoop, Kafka, Apache Flink, Presto. Databases: MySQL, PostgreSQL, MongoDB, Cassandra. Cloud: AWS, GCP, Azure, Databricks. Storage: S3, BigQuery, Redshift, Synapse Analytics, HDFS. Version Control: Git.