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 Himalayas