Data Engineer

PT.Japfa Comfeed Indonesia; Surabaya ꦱꦸꦫꦧꦪ; On-site; IDR 200.000.000 - 300.000.000; Full time

Job summary

A leading agricultural company in Surabaya seeks a Data Engineer to design, develop, and maintain ETL/ELT pipelines. The ideal candidate will collaborate with analytics teams to define data requirements and have strong SQL skills along with proficiency in data integration tools. A bachelor's degree in IT or related field is required, and fresh graduates are welcome. This role involves ensuring data accuracy and supporting business intelligence tools using cloud platforms.

Qualifications

• Bachelor's degree in a relevant field; Master’s degree is a plus.
• Experience in data engineering or data transformation is a plus; Fresh graduates are welcome.
• Proven experience in designing and maintaining ETL/ELT pipelines.
• Hands-on experience with cloud-based data platforms.
• Strong proficiency in SQL and at least one programming language.

Responsibilities

• Design, develop, and maintain ETL/ELT pipelines for data ingestion and transformation.
• Collaborate with teams to define data requirements and translate them into technical solutions.
• Ensure data accuracy and compliance with governance policies.
• Automate data workflows and monitor performance.

Skills

SQL

Python

ETL/ELT tools

Data modeling

Data integration

Education

Bachelor’s degree in Information Technology, Information Systems, or related field

Master’s degree

Tools

dbt

Informatica

Talend

Apache Airflow

Snowflake

Databricks

Google BigQuery

Amazon Redshift

Azure Synapse

Git

Jenkins

Job description

Responsibilities

• Design, develop, and maintain ETL/ELT pipelines for data ingestion, transformation, and integration.
• Collaborate with business and analytics teams to define data requirements and translate them into technical solutions.
• Build and optimize data models to support BI tools, dashboards, and advanced analytics.
• Ensure data accuracy, consistency, and compliance with data governance policies.
• Automate data workflows and monitor pipeline performance for reliability and scalability.
• Work closely with data architects and functional teams to improve overall data ecosystem efficiency.

Requirements

• Bachelor’s degree in Information Technology, Information Systems, or a related field; Master’s degree is a plus.
• Experience in data engineering, data transformation, or data integration roles is a plus. Fresh Grad is welcome.
• Proven experience designing, developing, and maintaining ETL/ELT data pipelines for structured and semi-structured data using tools such as dbt, Informatica, Talend, or Apache Airflow.
• Hands‑on experience working with cloud-based data platforms such as Snowflake, Databricks, Google BigQuery, Amazon Redshift, or Azure Synapse.
• Strong proficiency in SQL and practical experience with at least one programming language (e.g., Python, Scala, or Java) for automation and data manipulation.
• Demonstrated experience integrating and preparing data for Business Intelligence (BI) tools, such as Power BI, Qlik Sense, or Tableau, ensuring optimized data structures for visualization and analysis.
• Experience collaborating closely with business analysts, functional teams, and data consumers to translate business requirements into technical data solutions.
• Exposure to version control systems (Git) and CI/CD workflows, ideally within an Agile development environment.

Technical Skills

• SQL, Python, or Scala.
• TL/ELT tools (e.g., dbt, Informatica, Talend).
• Cloud data platforms (e.g., Snowflake, Databricks, BigQuery, Redshift).
• CI/CD and version control (e.g., Git, Jenkins).

Soft Skills

• Analytical Thinking – Ability to interpret complex data processes, identify root causes of issues, and propose practical solutions.
• Business Acumen – Understands business processes and can translate functional requirements into technical data solutions that add measurable value.
• Communication Skills – Able to communicate clearly with both technical and non-technical stakeholders, including data architects, analysts, and business users.
• Collaboration & Teamwork – Works effectively within cross-functional teams, fostering cooperation between IT, data, and business units.
• Problem‑Solving Mindset – Approaches challenges systematically, balancing technical precision with business needs.
• Attention to Detail – Ensures data accuracy and consistency throughout transformation and integration processes.
• Continuous Learning – Stays updated with emerging data technologies, engineering frameworks, and best practices.