Data Architect

Data Architect

Location : Pune & Bangalore

Qualifications :
• Minimum 8 years of experience in data architecture and analytics.
• In-depth knowledge of big data technologies and analytics tools, including Hadoop, Hive, Cassandra, MDM, Data Lakes, Streaming Data Management and Analytics, Spark, Kafka, Redis, Databricks.
• Proficient in programming languages such as Python, Java, Scala, and associated data-centric frameworks.
• Expertise in both on-premises and cloud databases, messaging applications, API development, and relevant Python libraries like FastAPI, Flask, Django.
• Strong understanding of cloud-based data architecture, APIs, and frameworks for Data Engineering and Analytics.
• Experience with Data Governance, Data Quality Management, Metadata Management, and Data Lineage.
• Exposure to enterprise-level data migration, data warehousing, and database modeling.
• Familiarity with microservices architecture and test-driven development methodologies.
• Proficiency in data visualization tools such as Tableau, Power BI, Matplotlib, Seaborn.
• Advanced skills in ETL processes, data visualization platforms like Talend, Tableau, Power BI.
• Ability to optimize performance and tune queries.
• Working knowledge of AI and ML concepts, including NLP, computer vision, and a range of ML algorithms and deep learning frameworks.
• Knowledgeable about GenAI tools and frameworks.

Role Description :
• Serve as a solution architect / consultant for end-to-end data engineering and analytics solutions, encompassing data handling, technology infrastructure, and modeling.
• Lead the design and development of solutions tailored to specific business objectives.
• Analyze and visualize data to understand its characteristics, identify distribution differences, and predict performance impacts in deployment scenarios.
• Ensure high data quality and integrity through rigorous verification and cleaning processes.
• Oversee data acquisition strategies to supplement existing datasets when necessary.
• Develop and implement data augmentation pipelines to enhance model training and performance.
• Collaborate closely with the technology team to plan, execute, and manage Data Engineering Product-based projects, while contributing to best practices.
• Design, implement, and support the creation and integration of APIs, models, and data pipelines.
• Deepen knowledge of data warehousing concepts, including technical architectures, infrastructure components, ETL / ELT processes, and reporting / analytic tools.
• Gain a comprehensive understanding of auxiliary practical concerns in production environments.
• Demonstrate strong communication, presentation, and consulting skills, capable of effectively articulating technical information and listening to and addressing client issues.