Data Engineer

Your role & responsibilities

1. Data Pipeline Development

• Assist in building and maintaining data ingestion and transformation pipelines
• Support ETL / ELT processes from multiple data sources
• Monitor pipeline jobs and help troubleshoot data issues

 

2. Data Platform & Storage

• Support development on data platforms such as: Data warehouses / data lakes
• Assist with table design, schema changes, and performance optimization
• Ensure data consistency and reliability

 

3. Cloud & Tooling Support

• Work with cloud data platforms (Azure / AWS – training provided)
• Support CI/CD, scheduling, and automation for data workflows
• Follow coding standards and best practices

 

4. Collaboration

• Work closely with Data Engineering and Data Analysts (to support reporting and dashboards)
• Participate in requirement discussions and technical reviews
• Document data pipelines and technical workflows

 

5. Learning & Growth

• Learn modern data engineering tools and architectures
• Gradually take ownership of small-to-medium data pipelines
• Follow guidance from Senior Data Engineers and Tech Leads

Your skills & qualifications

1. Experience

• 06 months – 2 years of experience in Data Engineering or related roles
• Fresh graduates with strong academic projects or internships are welcome

 

2. Technical Foundation

A solid technical foundation is essential for success in data engineering roles. Candidates should demonstrate the following core competencies:

• Basic Knowledge
  • SQL: Familiarity with writing queries, performing joins, and understanding basic optimization techniques.
  • Relational databases: Understanding how relational databases function and their role in data management.
• Core Concepts
  • ETL / data pipelines: Knowledge of Extract, Transform, Load processes and the architecture of data pipelines.
  • Data warehousing fundamentals: Awareness of data warehousing principles and their importance in storing and organizing large datasets.
• Programming Skills
  • Basic programming knowledge: Experience with Python or similar programming languages to support data engineering tasks.
• Nice to Have
  • Exposure to Cloud platforms (Azure / AWS) and Data tools (Airflow, dbt, Spark, etc.)
  • Basic understanding of analytics or AI data requirements

 

3. Soft Skills

• Logical thinking and problem-solving mindset
• Willingness to learn and adapt to new technologies
• Ability to work well in a team environment
• Attention to detail and responsibility for data quality

 

4. Education

• Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or related fields

Benefits for you

1. Career path development

• Learning & Career Development from Data Engineer (Fresher / Junior) → Data Engineer → Senior Data Engineer → Data Architect / Platform Lead
• Hands-on experience with enterprise-scale data platforms
• Clear technical growth path with mentoring
• Training and support for professional certifications

 

2. Compensation & Benefits

• Competitive salary and performance bonus
• 13th-month salary
• Health insurance, paid leave, and other benefits
• Annual performance review

 

3. Work Environment

• Flexible working policy
• Working hours: Monday – Friday, 9:00 AM – 6:00 PM
• Team building, company trips, and knowledge-sharing sessions

 

Why Join Datatyk

• Work on real enterprise analytics & AI projects
• Clear career growth toward Analytics Consultant / AI Analytics Lead
• Exposure to multiple industries and modern data platforms
• Competitive compensation and flexible working environment