Data Engineer

• Data Pipeline Development and Maintenance Building, modifying, and maintaining scalable data pipelines using cloud-based processing services and distributed systems.
• Cloud Data Processing Implementation Developing and enhancing data processing workflows using cloud functions and managed data services for ingestion and transformation.
• External Data Integration Implementing ingestion pipelines for external data sources while ensuring reliability and error-free processing.
• Data Quality and Validation Engineering Designing and applying automated data quality checks to ensure accuracy and integrity of critical datasets.
• Data Model Design and Optimization Designing and optimizing data models using modern data lake formats and layered architecture principles.
• Query Performance Optimization Improving data query performance while ensuring efficiency and adherence to data modeling standards.
• Data Governance and Compliance Ensuring data validation, governance rules, and monitoring processes are applied across all datasets.
• Documentation and Knowledge Management Documenting data processes, transformations, and technical decisions while maintaining version control best practices.

Must-Have:

• Python Development Experience Strong coding experience in Python with at least 5+ years of professional usage in data engineering environments.
• Test-Driven Development Expertise Experience applying TDD principles to ensure reliable and maintainable data engineering solutions.
• SQL and Data Schema Design Skills Strong SQL expertise for designing, optimizing, and maintaining efficient data schemas.
• AWS Data Services Experience Hands-on experience with AWS services such as Lambda, Glue, Athena, Aurora, EventBridge, and SageMaker.
• Production Data Pipeline Experience Proven experience building and maintaining production-grade data pipelines in cloud environments.
• Data Engineering Ownership Mindset Strong ownership mentality with ability to deliver features from design through production with measurable outcomes.
• Communication and Stakeholder Collaboration Clear communication skills when working with engineers, product teams, and data stakeholders.
• Pragmatic Engineering Approach Ability to balance engineering rigor with delivery speed and business needs.

Nice-to-Have:

• Data Architecture Knowledge Understanding of software architecture principles and design patterns used in scalable data systems.
• Data Modeling Experience Experience designing and maintaining structured data models for analytical and operational use cases.
• Pipeline Orchestration Knowledge Familiarity with workflow orchestration tools such as Airflow.
• Data Quality Framework Experience Knowledge of data validation and quality frameworks for ensuring reliable datasets.