3 years
0 Lacs
Posted:7 hours ago|
Platform:
On-site
Full Time
Must Have Skills:
1. Banking data analytics exposure (risk/finance)
2. SQL + Databricks + Azure stack proficiency
3. Data mapping, data modeling, and visualization (Power BI)
4. Spark/PySpark
5. Cloud Platform: Azure /AWS
• Bachelors or master’s degree in computer science, Information Technology, or a related field.
• Demonstrated expertise in working with Data Bricks for data processing and analysis.
• Experience in implementing and optimizing data solutions using multiple programming languages in an Azure environment.
Experience:
• Proven experience as a Data Analyst or in a similar role, years of hands-on experience in developing data pipelines and managing data infrastructure within Azure.
• 3+ years of experience in banking data
• Databricks and SQL experience required
• Experience in reporting, analysis and MIS teams preferred
• Worked on end-to-end Data Product deployment
Core Skills:
o Strong analytical and problem-solving skills to effectively design data solutions.
o Banking Retail, Business & Wholesale Product Knowledge and understanding
o Attention to detail and a commitment to delivering high-quality work.
o Excellent communication and teamwork skills to collaborate with cross-functional teams.
o Ability to manage time effectively and prioritize tasks in a dynamic work environment.
Technical Skills:
o Strong data querying and processing skills using SQL, Oracle, Apache Spark or similar language
o Data Visualization tools – Power BI, Business Objects, Crystal or similar tool
o Data Warehousing and ETL concepts
Competencies:
o Expert-level proficiency in SQL, Python with SAS/R/Spark as plus
o Deep understanding of distributed data processing frameworks, particularly Apache Spark.
o Experience with cloud platforms (Azure, AWS) — including Object storage, compute, networking, and data integration services.
o Familiarity with data modeling techniques, including schema design, partitioning, and performance tuning.
o Experience working with structured and semi-structured data formats (e.g., JSON, Parquet, Avro, XML).
o Knowledge of data governance, quality assurance, and compliance best practices.
o Solid grasp of core computer science fundamentals (e.g., data structures, search algorithms, queues) relevant to performance-critical data engineering
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