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Data Architect
Qualifications:
12+ years of experience.
Skills:
- Big data technologies and analytics: Majority of Hadoop, Hive, Cassandra, MDM, Data Lakes, Streaming data management and Analytics, Spark, Kafka, Redis, Databricks etc.
- Proficiency in programming languages such as Python, Java, Spark and associated libraries and frameworks.
- Hands-on and proficient in Databases (on-prem and Cloud), Messaging Apps, API Development and libraries.
- Very good knowledge of cloud-based architecture, APIs, frameworks for Data Engineering and Analytics.
- Knowledge of Data Governance & Data Quality Management, Metadata & Lineage Management,
- Exposure to enterprise level Data migration, Data Warehousing and DB modeling.
- Good understanding of Microservices and Test-Driven development.
- Familiarity with data visualization tools (e.g., Matplotlib, Seaborn).
- Python libraries for API development e.g. FastAPI, Flask, Django.
- Expertise standard ETL and Data Visualization tools like Talend, Tableau, Power BI etc.
- Expert in performance optimization and query tuning.
- Working knowledge on AI and ML including concepts in NLP, Computer Vision and breadth of ML algorithms, DL frameworks.
- Good knowledge of Gen AI based tools and frameworks.
Role Description:
- 1. Play a role of solution architect/consultant for end-to-end Data Engineering and Analytics solutions including data, technology infrastructure and models.
- 2. Design and development of solutions according to the business objective.
- 3. Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the solutions in the real world
- 4. Verifying data quality, and/or ensuring it via data cleaning
- 5. Supervising the data acquisition process if more data is needed
- 7. Work with the technology team to plan, execute, and deliver Data Engineering Product based projects. Contribute towards the best practices regarding handling the projects.
- 8. Provide technical design, implementation, and support services around the creation of APIs, Models and Integration Pipelines.
- 9. Knowledge of data warehousing concepts, including data warehouse technical architectures, infrastructure components, ETL/ ELT and reporting/analytic tools
- 10. Environments Understanding of the auxiliary practical concerns in production systems.
- 11. Strong communication, presentation and consulting skills, including technical writing skills and the ability to listen and understand client issue.
Seniority level
Seniority level
Mid-Senior level
Employment type
Job function
Job function
Engineering, Consulting, and Information TechnologyIndustries
IT Services and IT Consulting and Software Development
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