Senior Software Engineer- SQL, Data Engineer
Wells Fargo (company site, opens in a new tab)4+ yrsTelanganaOn-siteDirect career pageEst. 18–30 LPA
Posted on 4 Sept
Reach people at Wells Fargo
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Wells Fargo you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Lead moderately complex technical initiatives including design, coding, testing, debugging, and documentation within data engineering domains.
- Contribute to large scale planning, resolve technical challenges, and provide guidance to less experienced staff while collaborating across teams.
- Own design, requirements analysis, optimization, best practices definition, and code quality improvements for modernizing enterprise data platforms.
Minimum Requirements
- 4+ years of software engineering experience (or equivalent via work experience, training, military, education).
- Hands-on experience with Python, Spark, Iceberg, Hive; SQL development and tuning skills mandatory.
- Experience with ETL tools (Ab Initio preferred), cloud platforms (Azure or GCP), databases (Oracle, MS SQL, Teradata).
- Work Experience Required: Minimum 4 years software engineering; Notice Period: Not explicitly mentioned.
Ideal Candidate Profile
- Experienced in supporting and optimizing large enterprise-scale data environments with modern Data Warehousing, Data Lakes, and Lakehouse architectures.
- Skilled in implementing cloud-native, automated, CI/CD, and DevOps best practices with open-table formats like Iceberg.
- Familiarity with GenAI, Agentic AI, LLM adoption including RAG architectures, vector stores, and AI-driven data engineering enhancements.