GCP Data Engineer

Required Skills: Python, Pyspark, GCP, ETL - Big Data/Data Warehousing, Data Analytics, SQL, Git (GitHub, Git Lab, Bit Bucket, SVN), * Details BS or MS degree in computer science, computer engineering, or other technical discipline, or equivalent work experience. \n

• 10+ years of hands-on software development experience with Big Data & Analytics solutions GCP Cloud - Big Query, Airflow, DataProc, PubSub, Hadoop Hive, Spark, Python, shell scripting, Strong experience in designing, developing, and optimizing data pipelines for large-scale data processing, transformation, and analysis using Big Data and GCP technologies. \n

• Proficiency in SQL and database systems, with experience in designing and optimizing data models for performance and scalability. \n

• Experience of building event processing pipelines with Kafka or GCP PubSub. \n

• Design and development experience with Airflow, PubSub Kafka, Git, Jenkins is desirable. Knowledge of distributed (multi-tiered) systems, algorithms & relational databases. \n

• Strong Object-Oriented Programming skills and design patterns. \n

• Experience with CICD pipelines, Automated test frameworks, and source code management tools (XLR, Jenkins, Git). \n

• Good knowledge and experience with configuration management tools like GitHub Ability to analyze complex data engineering problems, propose effective solutions, and implement them effectively. \n

• Ability and willingness to learn, adopt and build solutions using the Enterprise frameworks established for Big Data Looks proactively beyond the obvious for continuous improvement opportunities. \n

• Communicates effectively with product and cross functional team. Willingness to learn new technologies and leverage them to their optimal potential. Understanding of various SDLC methodologies, familiarity with Agile & scrum ceremonies. \n

• Certifications in cloud platform (GCP Professional Data Engineer) is a plus. \n

• Provide engineering expertise to the team for developing features for Banking including Financial Crimes, Anti-money Laundering platforms Improve engineering practices, including driving resilience, predictability, and end efficiency through automation Partner with product teams to envision and deliver solutions which transform Financial Crimes Compliance using technology \n