Data Engineer

• 8+ years of overall experience in large-scale application development with recent mandatory platform for the secure and scalable deployment of AI agents into application contexts
• Minimum of 5+ years of proven experience in a Python and pyspark Engineering lead role focused on building enterprise-grade, high-volume ELT/ETL processes using the PySpark and Databricks ecosystem.
• Hands-on experience with agentic AI development using YAML, JSON, FAST API or Spring boot, Google ADK, LLM itegrations, including Devin.AI or Github Copilot, and integrating models via platforms like MCP using advanced prompt engineering.
• Proven experience developing and automating microservice integrations to support data-intensive applications.
• Proficiency in at least one programming language commonly used for data analytics, engineering, such as Python or Scala.
• Strong SQL skills and experience with various relational databases.
• Deep understanding of data modeling, data warehousing concepts, Data Mesh architecture, and data federation.
• Excellent communication, collaboration, and problem-solving skills.

Preferred:

• Experience with cloud-based Big Data platforms (e.g., Cloudera, Databricks, AWS, Azure, GCP).
• Experience with frontend technologies such as Angular or React JS for building data-driven application interfaces.
• Practical experience applying AI/ML techniques to solve real-world business problems.
• Familiarity with containerization technologies (e.g., Docker, Kubernetes).
• Experience in data engineering within the banking retail products domain (e.g., Cards, Mortgage, Deposits, Wealth Management).
• Relevant industry certifications (e.g., AWS Certified Big Data - Specialty, Azure Data Engineer Associate).

Education

• Bachelor s degree in Computer Science, Engineering, or a related field.
• Master s degree is a plus.

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