The Data Engineer is responsible for designing, building, and maintaining scalable data pipelines and architectures to support analytics, reporting, and business intelligence. This role ensures data integrity, accessibility, and performance across multiple systems.
Key Responsibilities
Data Pipeline Development
Design, develop, and optimize ETL/ELT processes for structured and unstructured data.
Implement data ingestion from various sources (APIs, databases, cloud storage).
Data Architecture
Build and maintain data warehouses, data lakes, and real-time streaming systems.
Ensure data models are efficient, scalable, and aligned with business needs.
Data Quality \& Governance
Implement data validation, cleansing, and transformation processes.
Ensure compliance with data security and privacy regulations.
Collaboration
Work closely with Data Scientists, Analysts, and Business teams to deliver reliable datasets.
Partner with DevOps for deployment and monitoring of data solutions.
Required Skills \& Qualifications
Technical Skills
Strong proficiency in SQL and relational databases.
Experience with big data technologies (Hadoop, Spark) and cloud platforms (AWS, Azure, GCP).
Knowledge of data pipeline tools (Airflow, Kafka, Glue).
Programming skills in Python, Java, or Scala.
Experience
3–7 years in data engineering or related roles.
Hands-on experience with data modeling and performance tuning.
Certifications (Preferred)
AWS Certified Data Analytics or Google Professional Data Engineer.
Knowledge of CI/CD and containerization (Docker, Kubernetes).
Soft Skills
Strong problem-solving and analytical skills.
Excellent communication and teamwork abilities.
Ability to manage multiple projects in a fast-paced environment.