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As a Senior Data Engineer, you will use various methods to transform raw data into useful data systems. Collaborate closely with cross-functional teams to ensure the availability, reliability, and accessibility of data for analytics and decision-making purposes. Overall, you’ll strive for efficiency by aligning data systems with business goals. To succeed in this data engineering position, you should have strong analytical skills and the ability to combine data from different sources. Data engineer skills also include familiarity with several programming languages and knowledge of learning machine methods. Responsibilities Design, develop, and maintain scalable data pipelines and ETL processes using tools such as Apache Spark, Apache Kafka, and Apache Airflow to ingest, process, and transform large volumes of data from various sources. Deploy Data Pipeline on different Data Processing Product - DataFlow (Apache Beam), DataProc (Hadoop/Spark), Data Fusion, Cloud Composer(Airflow) Building distributed systems and data stores Collaborating with and supporting data science, marketing, and customer success teams in data acquisition and tool integration Configuration of Google Cloud Platform services Implement and optimize data storage solutions, including data warehouses (e.g., Amazon Redshift, Google BigQuery), data lakes (e.g., AWS S3, Azure Data Lake Storage), and NoSQL databases (e.g., MongoDB, Cassandra). Work closely with data architects to design and implement efficient data models using dimensional modeling techniques (e.g., star schema, snowflake schema) that support business requirements and enable effective data analysis. Collaborate with data scientists and analysts to understand data requirements and develop solutions to support advanced analytics and machine learning initiatives, including model training and deployment. Implement data quality checks, monitoring and custom scripts to ensure the accuracy, completeness, and reliability of data. Participate in troubleshooting and resolving data-related issues, ensuring timely resolution and minimal disruption to business operations. Stay updated on emerging technologies and best practices in data engineering, including cloudnative solutions and serverless architectures, and contribute to the continuous improvement of data platforms and infrastructure. Solid knowledge of Google’s BigQuery for effective data processing Proposing and implementing repetitive tasks automation. Providing support for development teams in deployment-related topics. Modernizing data lakes and data warehouses