Key Responsibilities
- Data Architecture Design: Contribute to the design of scalable and secure data solutions, ensuring alignment with business and technical needs.
- ETL/ELT Pipeline Development: Develop and optimize efficient pipelines to ingest, transform, and load data from diverse sources into structured formats for analytics.
- Data Source Analysis: Analyze structured and unstructured data sources, recommending strategies for ingestion, processing, and classification.
- Data Layer Development: Assist in building a robust data layer that supports both batch and real-time processing.
- Data Ingestion & Cleansing: Implement strategies for validating and cleansing data to ensure quality and compliance with governance policies.
- Query Optimization: Write and optimize SQL queries to improve performance and efficiency across large datasets.
- Data Migration: Support migration projects from legacy systems to cloud platforms, ensuring accuracy and minimal downtime.
- Performance Monitoring: Monitor data workflows, troubleshoot bottlenecks, and propose improvements.
- Collaboration: Work closely with analysts, scientists, and business teams to translate requirements into data models.
- Data Governance & Security: Apply governance standards and security best practices, ensuring compliance with regulations (e.g., GDPR).
- Continuous Learning: Stay updated on emerging tools and practices, contributing to team innovation and improvement.
Qualifications
- 5+ years of experience in data engineering or related roles.
- Strong knowledge of SQL, relational databases, and query optimization.
- Hands-on experience with ETL/ELT tools and cloud platforms (AWS, Azure, or GCP).
- Familiarity with data governance, security, and compliance frameworks.
- Solid understanding of batch and real-time data processing.
- Collaborative mindset with ability to work across technical and business teams.
Originally posted on