4 Sonatype Nexus Jobs

Key Responsibilities: Lead and mentor a team of developers to ensure high-quality delivery. Collaborate with tech leads, architects, DevOps, Data teams, Business Analysts (BA), Project Managers (PM), and C-Level executives to brainstorm and refine technical solutions. Articulate and align technical and business strategies effectively. Follow disciplined SDLC processes, ensuring quality and seamless team integration. Contribute actively in discussions, proposing innovative solutions across architecture, features, and business needs. Ensure the system operates with minimal downtime (the current system has had only 3 hours of downtime in 4 years). Review code, ensuring adherence to quality standards. Understand and leverage GenAI tools in SDLC. Be proficient in AWS, DevOps, Data, and SDLC processes. Technical Landscape: The platform is built using a microservice and serverless architecture on AWS cloud-based services, with the following technologies: Backend : Flask, PostgreSQL (Aurora), SQLAlchemy, REST API, Celery. Frontend : React JS. Core Data and Serverless : AWS Redshift, S3, Glue, Aurora RDS. Deployment & Infrastructure : Terraform, Jenkins, Docker, Sonatype Nexus, ECR, EKS, Lambda, etc. AWS Services : IAM, CloudWatch, CloudTrail, SQS, SNS, Elasticache, API Gateway, Cognito, OpenSearch, Secrets Manager, SageMaker, etc. Location : On-site at Mohali, Chandigarh, Gurgaon, or Noida.

What you’ll do As a Data Engineer – Data Modeling, you will be responsible for: Data Modeling & Schema Design Developing conceptual, logical, and physical data models to support enterprise data requirements. Designing schema structures for Apache Iceberg tables on Cloudera Data Platform. Collaborating with ETL developers and data engineers to optimize data models for efficient ingestion and retrieval. Data Governance & Quality Assurance Ensuring data accuracy, consistency, and integrity across data models. Supporting data lineage and metadata management to enhance data traceability. Implementing naming conventions, data definitions, and standardization in collaboration with governance teams. ETL & Data Pipeline Support Assisting in the migration of data from IIAS to Cloudera Data Lake by designing efficient data structures. Working with Denodo for data virtualization, ensuring optimized data access across multiple sources. Collaborating with teams using Talend Data Quality (DQ) tools to ensure high-quality data in the models. Collaboration & Documentation Working closely with business analysts, architects, and reporting teams to understand data requirements. Maintaining data dictionaries, entity relationships, and technical documentation for data models. Supporting data visualization and analytics teams by designing reporting-friendly data models. Required education Bachelor's Degree Preferred education Master's Degree Required technical and professional expertise 4-7 years of experience in data modeling, database design, and data engineering. Hands-on experience with ERwin Data Modeler for creating and managing data models. Strong knowledge of relational databases (PostgreSQL) and big data platforms (Cloudera, Apache Iceberg). Proficiency in SQL and NoSQL database concepts. Understanding of data governance, metadata management, and data security principles. Familiarity with ETL processes and data pipeline optimization. Strong analytical, problem-solving, and documentation skills. Preferred technical and professional experience Experience working on Cloudera migration projects. Exposure to Denodo for data virtualization and Talend DQ for data quality management. Knowledge of Kafka, Airflow, and PySpark for data processing. Familiarity with GitLab, Sonatype Nexus, and CheckMarx for CI/CD and security compliance. Certifications in Data Modeling, Cloudera Data Engineering, or IBM Data Solutions.

Who you areA highly skilled Data Engineer specializing in Data Modeling with experience in designing, implementing, and optimizing data structures that support the storage, retrieval and processing of data for large-scale enterprise environments. Having expertise in conceptual, logical, and physical data modeling, along with a deep understanding of ETL processes, data lake architectures, and modern data platforms. Proficient in ERwin, PostgreSQL, Apache Iceberg, Cloudera Data Platform, and Denodo. Possess ability to work with cross-functional teams, data architects, and business stakeholders ensures that data models align with enterprise data strategies and support analytical use cases effectively. What you’ll doAs a Data Engineer – Data Modeling, you will be responsible for: Data Modeling & Architecture Designing and developing conceptual, logical, and physical data models to support data migration from IIAS to Cloudera Data Lake. Creating and optimizing data models for structured, semi-structured, and unstructured data stored in Apache Iceberg tables on Cloudera. Establishing data lineage and metadata management for the new data platform. Implementing Denodo-based data virtualization models to ensure seamless data access across multiple sources. Data Governance & Quality Ensuring data integrity, consistency, and compliance with regulatory standards, including Banking/regulatory guidelines. Implementing Talend Data Quality (DQ) solutions to maintain high data accuracy. Defining and enforcing naming conventions, data definitions, and business rules for structured and semi-structured data. ETL & Data Pipeline Optimization Supporting the migration of ETL workflows from IBM DataStage to PySpark, ensuring models align with the new ingestion framework. Collaborating with data engineers to define schema evolution strategies for Iceberg tables. Ensuring performance optimization for large-scale data processing on Cloudera. Collaboration & Documentation Working closely with business analysts, architects, and developers to translate business requirements into scalable data models. Documenting data dictionary, entity relationships, and mapping specifications for data migration. Supporting reporting and analytics teams (Qlik Sense/Tableau) by providing well-structured data models. Required education Bachelor's Degree Preferred education Master's Degree Required technical and professional expertise 4-7 years of experience in data modeling, database design, and data engineering. Hands-on experience with ERwin Data Modeler for creating and managing data models. Strong knowledge of relational databases (PostgreSQL) and big data platforms (Cloudera, Apache Iceberg). Proficiency in SQL and NoSQL database concepts. Understanding of data governance, metadata management, and data security principles. Familiarity with ETL processes and data pipeline optimization. Strong analytical, problem-solving, and documentation skills. Preferred technical and professional experience Experience working on Cloudera migration projects. Exposure to Denodo for data virtualization and Talend DQ for data quality management. Knowledge of Kafka, Airflow, and PySpark for data processing. Familiarity with GitLab, Sonatype Nexus, and CheckMarx for CI/CD and security compliance. Certifications in Data Modeling, Cloudera Data Engineering, or IBM Data Solutions.

Who you areA highly skilled Data Engineer specializing in Data Modeling with experience in designing, implementing, and optimizing data structures that support the storage, retrieval and processing of data for large-scale enterprise environments. Having expertise in conceptual, logical, and physical data modeling, along with a deep understanding of ETL processes, data lake architectures, and modern data platforms. Proficient in ERwin, PostgreSQL, Apache Iceberg, Cloudera Data Platform, and Denodo. Possess ability to work with cross-functional teams, data architects, and business stakeholders ensures that data models align with enterprise data strategies and support analytical use cases effectively. What you’ll doAs a Data Engineer – Data Modeling, you will be responsible for: Data Modeling & Architecture Designing and developing conceptual, logical, and physical data models to support data migration from IIAS to Cloudera Data Lake. Creating and optimizing data models for structured, semi-structured, and unstructured data stored in Apache Iceberg tables on Cloudera. Establishing data lineage and metadata management for the new data platform. Implementing Denodo-based data virtualization models to ensure seamless data access across multiple sources. Data Governance & Quality Ensuring data integrity, consistency, and compliance with regulatory standards, including Banking/regulatory guidelines. Implementing Talend Data Quality (DQ) solutions to maintain high data accuracy. Defining and enforcing naming conventions, data definitions, and business rules for structured and semi-structured data. ETL & Data Pipeline Optimization Supporting the migration of ETL workflows from IBM DataStage to PySpark, ensuring models align with the new ingestion framework. Collaborating with data engineers to define schema evolution strategies for Iceberg tables. Ensuring performance optimization for large-scale data processing on Cloudera. Collaboration & Documentation Working closely with business analysts, architects, and developers to translate business requirements into scalable data models. Documenting data dictionary, entity relationships, and mapping specifications for data migration. Supporting reporting and analytics teams (Qlik Sense/Tableau) by providing well-structured data models. Required education Bachelor's Degree Preferred education Master's Degree Required technical and professional expertise Experience in Cloudera migration projects in the banking or financial sector. Knowledge of PySpark, Kafka, Airflow, and cloud-native data processing. Experience with Talend DQ for data quality monitoring. Preferred technical and professional experience Experience in Cloudera migration projects in the banking or financial sector. Knowledge of PySpark, Kafka, Airflow, and cloud-native data processing. Experience with Talend DQ for data quality monitoring. Familiarity with graph databases (DGraph Enterprise) for data relationships. Experience with GitLab, Sonatype Nexus, and CheckMarx for CI/CD and security compliance. IBM, Cloudera, or AWS/GCP certifications in Data Engineering or Data Modeling.