Data Engineer

Results Driven 

• Delivers reliable, scalable data pipelines that support analytics, reporting, and data science initiatives. 

• Translates business and operational requirements into effective warehouse designs and data models. 

• Diagnoses and resolves issues in high-volume data processing systems to minimize downtime and protect data integrity. 

• Ensures warehouse architecture supports performance, accuracy, and enterprise analytics needs. 

Takes Ownership 

• Designs, develops, and maintains automated, real-time data pipelines from multiple source systems into the data lake and warehouse. 

• Maintains deep understanding of source systems and downstream consumers to champion data usability and reliability. 

• Implements auditing, logging, and data quality controls to ensure consistency and trust in data workflows. 

• Owns issue resolution across ingestion, transformation, and delivery layers, coordinating with partners as needed. 

Drives Efficiency 

• Optimizes pipeline and query performance using SQL, Python, profiling tools, and tuning techniques. 

• Implements scalable Snowflake development workflows that reduce rework and accelerate delivery. 

• Applies strong ETL/ELT design principles to streamline data movement and lifecycle management. 

• Participates in architecture reviews and contributes to best practices that improve warehouse scalability and maintainability. 

Innovative 

• Researches and adopts emerging technologies and modern architectural patterns to improve performance and scalability. 

• Enhances warehouse capabilities to better support advanced analytics and data science initiatives. 

• Experiments with new orchestration, automation, and monitoring approaches to improve reliability and observability. 

• Contributes to evolving data governance standards and quality frameworks across the organization. 

Requirements 

        Exp- 3 to 7 years

• Strong SQL expertise with proven ability to write, optimize, and maintain complex queries 

• Experience designing and implementing modern, architecture-based data warehouses in enterprise environments 

• Hands-on experience with cloud data warehouses such as Snowflake, Redshift, or BigQuery 

• Proficiency in scripting or software engineering languages such as Python, Bash, or JavaScript 

• Strong understanding of data modeling, ETL/ELT design, and data lifecycle management 

• Ability to work with large-scale datasets from diverse source systems 

• Ability to communicate technical concepts clearly to both technical and non-technical stakeholders