Data Engineer

Job Summary :

As a Data Engineer , you will be responsible for building and maintaining the data infrastructure that supports data analysis, data science, and business intelligence efforts. This includes designing, developing, and optimizing data pipelines, ensuring data quality, and implementing data storage solutions. You will work closely with data scientists, analysts, and other stakeholders to deliver high-quality data solutions.

Key Responsibilities :

1. Data Pipeline Development :

• Design, develop, and maintain robust, scalable data pipelines to process large volumes of structured and unstructured data.
• Implement ETL (Extract, Transform, Load) processes to ensure clean, reliable, and accessible data for analysis.
• Optimize data pipelines for performance, scalability, and reliability.

2. Data Architecture and Design :

• Design and implement data storage solutions, including databases (SQL and NoSQL), data lakes, and cloud-based storage platforms (e.g., AWS , Google Cloud , Azure ).
• Architect efficient and scalable data models to support various use cases.
• Ensure data consistency, integrity, and security across different systems and environments.

3. Collaboration with Data Scientists and Analysts :

• Work closely with data scientists, data analysts, and business stakeholders to understand data requirements and deliver the necessary data infrastructure.
• Support data scientists with data preparation and preprocessing for machine learning models and analytics.
• Assist in transforming raw data into usable formats for reporting, analysis, and decision-making.

4. Data Quality and Monitoring :

• Implement data quality checks to ensure that data is accurate, complete, and timely.
• Monitor data pipelines and resolve issues related to data failures or inconsistencies.
• Continuously improve data workflows and processes to enhance data reliability and availability.

5. Performance Optimization :

• Optimize data storage and querying performance, ensuring that data systems scale efficiently.
• Analyze and improve the efficiency of data processes and workflows, particularly in high-volume environments.

6. Automation and Maintenance :

• Automate repetitive data processes and workflows to improve operational efficiency.
• Maintain and monitor the performance of existing data infrastructure, ensuring it runs smoothly and without interruption.

7. Documentation :

• Create and maintain comprehensive documentation for data pipelines, data models, and infrastructure.
• Ensure that best practices and standards for data engineering are documented and followed by the team.

Required Skills and Qualifications :

• Education : Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field.
• Experience :
  • Proven experience as a Data Engineer , Data Analyst , or similar role.
  • Experience working with large datasets and building data pipelines.
  • Proficiency in SQL and experience with relational and NoSQL databases (e.g., PostgreSQL , MySQL , MongoDB , Cassandra ).
  • Hands-on experience with ETL tools (e.g., Apache Nifi , Talend , Airflow ).
  • Familiarity with cloud platforms and services, such as AWS (e.g., S3 , Redshift , Glue ), Azure , or Google Cloud .
  • Strong programming skills in languages such as Python , Java , Scala , or R .
  • Experience with data warehousing solutions and frameworks like Snowflake , BigQuery , or Redshift .

Preferred Skills :

• Experience with streaming data platforms (e.g., Apache Kafka , Apache Flink , Kinesis ).
• Familiarity with containerization technologies like Docker and Kubernetes .
• Knowledge of data governance and security best practices .
• Experience with machine learning or data science workflows is a plus.
• Familiarity with business intelligence tools such as Tableau , Power BI , or Looker .
• Experience with data lakes and unstructured data processing.

Personal Attributes :

• Strong analytical and problem-solving skills.
• Ability to work in a fast-paced, collaborative, and dynamic environment.
• Excellent communication skills to effectively work with cross-functional teams.
• Detail-oriented with a focus on delivering high-quality data solutions.
• Proactive attitude with a focus on continuous improvement.