Job details
ResponsibilitiesDesign end-to-end data solutions, including solution architecture, data modeling, and data flow diagrams, to address complex business requirements.Design, develop, and maintain scalable data pipeline architecture to support the company's and clients' data processing needs.Implement best practices for data ingestion, storage, and processing to ensure data integrity, reliability, and security.Architect and implement Big Data processing workflows to handle large volumes of data with high throughput and low latency, ensuring timely and accurate data delivery.Drive the adoption of data engineering best practices and standards across the organization, including data governance, data security, and data quality.Lead and mentor junior team members, providing guidance on best practices and technical solutions.Maintain consistent communication with the Project Team Lead and Project Manager, providing clear updates on task status and promptly escalating any issues or potential delays.Good to have:Design and develop real-time streaming data pipelines using technologies such as Apache Kafka, Apache Pulsar, or cloud-native streaming services (e.g., AWS Kinesis, Google Cloud Pub/Sub).Lead data architecture reviews and provide recommendations for optimizing performance, scalability, and cost efficiency.Collaborate with data scientists and analysts to understand data requirements and provide technical expertise in data modeling and analytics.What we are looking for?Proficiency in designing, building, and maintaining scalable ETL data processing systems using Python, Java, Scala, or similar programming languages.Expertise in SQL - Demonstrate a strong grip on SQL to process and analyze various forms of data.Experience working with Cloud Deployment Environments, preferably GCP, Azure or AWS.Experience in creating and managing CI/CD pipelines using Azure DevOps, Jenkins, GoCD, or similar technologies.Proficiency in orchestration tools like Apache Airflow, Azure Data Factory, AWS Data Pipelines, or similar.Familiarity with version control systems like GitHub and CI/CD concepts.Understanding of Data Modeling and Data Warehousing techniques, including star schema or raw data vault.Experience working with large-scale data sets, including structured and unstructured data formats like Parquet, Avro, JSON, etc.Good to have:Knowledge of big data processing technologies such as Hadoop, Apache HBase, Apache Hive, or similar.Experienced with designing and implementing Data Governance policies covering Data Accessibility, Security, Management, Quality etc.
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