Data Engineer - Tech Kinect

Responsibilities:

• Data Architecture Design: Design and develop scalable efficient and robust data architecture solutions to support data storage processing and retrieval requirements.
• Data Pipeline Development: Build and maintain data pipelines to efficiently extract transform and load (ETL) data from various sources into the data warehouse or data lake.
• Data Integration: Integrate data from multiple systems databases and APIs ensuring data consistency accuracy and quality.
• Data Transformation and Cleansing: Perform data transformation cleansing and validation to ensure data integrity and conformity with defined business rules and standards.
• Data Modeling: Design and implement data models that meet the analytical and reporting needs of the organization including dimensional modeling star schemas and data marts.
• Data Warehousing: Design and manage data warehousing solutions including schema design indexing partitioning and performance optimization.
• Data Governance and Security: Implement data governance practices and ensure data security and privacy standards are upheld throughout the data lifecycle.
• Performance Optimization: Identify and optimize performance bottlenecks in data processing and storage including query optimization indexing and data partitioning.
• Data Monitoring and Maintenance: Monitor data pipelines data quality and system performance and proactively address any issues or anomalies.
• Documentation: Create and maintain technical documentation including data flow diagrams system architecture and data dictionaries.

Requirements:

Education and Experience:

• Bachelors or Masters degree in Computer Science Information Systems or a related field.
• Proven work experience as a data engineer or in a similar role.
• Strong understanding of data engineering principles techniques and best practices.

Technical Skills:

• Proficiency in programming languages like Python Java or Scala.
• Experience with SQL and database technologies (e.g. Oracle MySQL PostgreSQL).
• Knowledge of big data technologies and frameworks (e.g. Hadoop Spark Kafka).
• Familiarity with cloud platforms and services (e.g. AWS Azure Google Cloud).
• Understanding of data modeling techniques and concepts.
• Experience with data integration tools and ETL frameworks (e.g. Apache Airflow Informatica Talend).
• Knowledge of data warehousing concepts and methodologies.
• Strong problemsolving and analytical skills.

Soft Skills:

• Excellent communication and collaboration skills.
• Ability to work effectively in a team environment.
• Strong attention to detail and organizational skills.
• Ability to manage multiple tasks and prioritize effectively.
• Continuous learning mindset to keep up with evolving data engineering technologies and practices.