Must-Have Skills: 5+ years of experience as a Big Data Engineer 3+ years of experience with Apache Spark, Hive, HDFS, and Beam (optional) Strong proficiency in SQL and either Scala or Python Experience with ETL processes and working with structured and unstructured data 2+ years of experience with Cloud Platforms (GCP, AWS, or Azure) Hands-on experience with software build management tools like Maven or Gradle Experience in automation, performance tuning, and optimizing data pipelines Familiarity with CI/CD, serverless computing, and infrastructure-as-code practices Good-to-Have Skills: Experience with Google Cloud Services (BigQuery, Dataproc, Dataflow, Composer, DataStream) Strong knowledge of data pipeline development and optimization Familiarity with source control tools (SVN/Git, GitHub) Experience working in Agile environments (Scrum, XP, etc.) Knowledge of relational databases (SQL Server, Oracle, MySQL) Experience with Atlassian tools (JIRA, Confluence, GitHub) Key Responsibilities: Extract, transform, and load (ETL) data from multiple sources using Big Data technologies Develop, enhance, and support data ingestion jobs using GCP services like Apache Spark, Dataproc, Dataflow, BigQuery, and Airflow Work closely with senior engineers and cross-functional teams to improve data accessibility Automate manual processes, optimize data pipelines, and enhance infrastructure for scalability Modify data extraction pipelines to follow standardized, reusable approaches Optimize query performance and data access techniques in collaboration with senior engineers Follow modern software development practices, including microservices, CI/CD, and infrastructure-as-code Participate in Agile development teams, ensuring best practices for software engineering and data management Preferred Qualifications: Bachelor's degree in Computer Science, Systems Engineering, or a related field Self-starter with strong problem-solving skills and adaptability to shifting priorities Cloud certifications (GCP, AWS, or Azure) are a plus