Lead Software Engineer

Bachelor's or master's degree in computer science, engineering, mathematics or

a related discipline, or equivalent practical experience.

• Substantial professional experience designing and operating enterprise data
  
  

pipelines and data warehouses in production.

• Advanced SQL skills and strong hands-on experience with Python and/or
  
  

PySpark for data processing and automation.

• Deep experience with Snowflake, including performance optimization, security,
  
  

workload management and scalable data modeling.

• Experience building ETL/ELT solutions with dbt, Informatica PowerCenter or
  
  

comparable transformation and integration tools.

• Hands-on experience implementing CI/CD pipelines for data engineering
  
  

workloads using Git-based workflows and automated testing/deployment tools.

• Experience building automated data quality systems with measurable rules,
  
  

alerting, lineage and issue-resolution workflows.

• Strong grounding in data profiling, cleansing, standardization, reconciliation and
  
  

production data validation.

• Ability to gather ambiguous requirements, make sound design decisions and
  
  

communicate effectively with technical and non-technical partners.

Preferred Qualifications

• Snowflake SnowPro Core or comparable cloud data certification.
• Experience with REST API, event and/or streaming data, and high-volume
  
  

security or operational datasets.

• Familiarity with data observability and quality frameworks such as dbt tests,
  
  

Great Expectations, Soda or Deequ.

• Experience delivering data solutions in regulated, security-sensitive, healthcare,
  
  

telecommunications or audit-oriented environments.

• Demonstrated technical leadership, mentoring or delivery coordination across a
  
  

small engineering team.

Education Qualificaiton

B Tech/ M Tech

Roles & Responsibilities

• Design and deliver scalable pipelines. Build batch and near-real-time ETL/ELT
  
  

pipelines using SQL, Python and PySpark, with Snowflake as a primary cloud data platform.

• Engineer reliable data models. Create dimensional, normalized and analytical models
  
  

that support efficient querying, traceability and reuse across enterprise reporting and analytics.

• Own data quality by design. Implement automated profiling, schema checks,
  
  

reconciliation, completeness, freshnes