Senior 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 6+ 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.
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 6+ 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.