4+ HDFS Jobs in Mumbai | HDFS Job openings in Mumbai

Experience: 12-15 Years

Key Responsibilities: 

• Client Engagement & Requirements Gathering: Independently engage with client stakeholders to
• understand data landscapes and requirements, translating them into functional and technical specifications.
• Data Architecture & Solution Design: Architect and implement Hadoop-based Cloudera CDP solutions,
• including data integration, data warehousing, and data lakes.
• Data Processes & Governance: Develop data ingestion and ETL/ELT frameworks, ensuring robust data governance and quality practices.
• Performance Optimization: Provide SQL expertise and optimize Hadoop ecosystems (HDFS, Ozone, Kudu, Spark Streaming, etc.) for maximum performance.
• Coding & Development: Hands-on coding in relevant technologies and frameworks, ensuring project deliverables meet stringent quality and performance standards.
• API & Database Management: Integrate APIs and manage databases (e.g., PostgreSQL, Oracle) to support seamless data flows.
• Leadership & Mentoring: Guide and mentor a team of data engineers and analysts, fostering collaboration and technical excellence.

Skills Required:

• a. Technical Proficiency:
• • Extensive experience with Hadoop ecosystem tools and services (HDFS, YARN, Cloudera
• Manager, Impala, Kudu, Hive, Spark Streaming, etc.).
• • Proficiency in programming languages like Spark, Python, Scala and a strong grasp of SQL
• performance tuning.
• • ETL tool expertise (e.g., Informatica, Talend, Apache Nifi) and data modelling knowledge.
• • API integration skills for effective data flow management.
• b. Project Management & Communication:
• • Proven ability to lead large-scale data projects and manage project timelines.
• • Excellent communication, presentation, and critical thinking skills.
• c. Client & Team Leadership:
• • Engage effectively with clients and partners, leading onsite and offshore teams.

• Architectural Leadership:
• Design and architect robust, scalable, and high-performance Hadoop solutions.
• Define and implement data architecture strategies, standards, and processes.
• Collaborate with senior leadership to align data strategies with business goals.
• Technical Expertise:
• Develop and maintain complex data processing systems using Hadoop and its ecosystem (HDFS, YARN, MapReduce, Hive, HBase, Pig, etc.).
• Ensure optimal performance and scalability of Hadoop clusters.
• Oversee the integration of Hadoop solutions with existing data systems and third-party applications.
• Strategic Planning:
• Develop long-term plans for data architecture, considering emerging technologies and future trends.
• Evaluate and recommend new technologies and tools to enhance the Hadoop ecosystem.
• Lead the adoption of big data best practices and methodologies.
• Team Leadership and Collaboration:
• Mentor and guide data engineers and developers, fostering a culture of continuous improvement.
• Work closely with data scientists, analysts, and other stakeholders to understand requirements and deliver high-quality solutions.
• Ensure effective communication and collaboration across all teams involved in data projects.
• Project Management:
• Lead large-scale data projects from inception to completion, ensuring timely delivery and high quality.
• Manage project resources, budgets, and timelines effectively.
• Monitor project progress and address any issues or risks promptly.
• Data Governance and Security:
• Implement robust data governance policies and procedures to ensure data quality and compliance.
• Ensure data security and privacy by implementing appropriate measures and controls.
• Conduct regular audits and reviews of data systems to ensure compliance with industry standards and regulations.