Data Engineer

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

4+ 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.