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