Data Architect

4 months ago

Rajkot, India Calsoft Full time

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:

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

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