YMinds.AI
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1 to 50 Employees · Bengaluru, India
Hiring: Engineering
What employees say
+ nothing as such young team a lot to learn
− no maintaining code false ai hype
More reviews on the company profile.
Interview experience · Python Backend Developer
Terrible intervew proess, they have an AI that takes the interview. First Round: It was fine, the AI bot asked some basic questions and itwas fine Second Round: It was supposed to be a 60 mins coding round by the AI,
Average difficulty · India · 3 reports
More interview insightsJob description
Overview
Our client is seeking a Data Engineer with 5+ years of hands-on experience to build an AI-enablement layer on top of its enterprise Finance data platform.
Responsibilities
- Build AI-ready, governed data products on Snowflake and/or Databricks with clearly defined KPIs, hierarchies, and business semantics.
- Develop semantic layers and trusted datasets for BI reporting and natural-language querying.
- Configure and optimize Snowflake Cortex capabilities and/or Databricks Genie spaces for accuracy and business relevance.
- Build RAG pipelines and conversational analytics applications using Streamlit or Databricks Apps.
- Develop and maintain ETL/ELT pipelines using dbt, Airflow, Snowpark, PySpark, SQL, and Python.
- Implement data governance controls, including RBAC, row- and column-level security, masking, lineage, metadata management, and auditability.
- Collaborate with Finance stakeholders while optimizing platform performance, compute costs, and LLM usage.
Qualifications
- 5+ years of hands-on data engineering experience on cloud data platforms.
- Demonstrated real-world project delivery experience with Snowflake and/or Databricks.
- Direct hands-on experience building, configuring, and tuning Snowflake Cortex capabilities or Databricks Genie spaces with semantic models in production or advanced pilots.
- Proven experience delivering semantic layers and trusted, governed data products.
- Strong understanding of KPI definitions, business hierarchies, data semantics, and business glossary alignment.
- Strong hands-on expertise in dbt, PySpark, Snowpark, SQL, and Python.
- Data governance experience within regulated enterprise environments.
- SnowPro Advanced or Databricks Data Engineer Professional certification.
- Knowledge of LLM cost optimization through token management, caching, and model routing.
- Familiarity with Finance data models, KPIs, reporting processes, and governance requirements.
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