Data Engineer, Finance Analytics

1 week ago

Kuala Lumpur, Kuala Lumpur, Malaysia Averis Full time

Hello, we are Averis

Averis Information Technology is a global IT services organization headquartered in Kuala Lumpur that designs and delivers IT solutions for large enterprises, to drive economies of scale and business transformation. Our core areas of expertise are: infrastructure and networking, ERP and procurement systems, cybersecurity and data governance, digital workplace and end-user management. Our journey started in 2006, and today, more than 300 IT professionals are servicing major customers in resource-based manufacturing industries such as i.e. paper, packaging and tissue, edible oils, and energy which collectively encompass $35B of assets and 80,000 employees sited across 32 locations globally.

Role Summary

  • Join our Data Engineering team to help build reliable data pipelines and analytics foundations that power decision-making across the business.
  • You will develop ETL/ELT workflows, maintain data quality, and collaborate with senior engineers, analysts, and data scientists in a fast-paced environment.

Key Responsibilities

  • Build, test, and maintain data pipelines to ingest, transform, and load data from diverse sources (batch and streaming).
  • Write clean, efficient SQL and Python/Scala for data processing on platforms such as Apache Spark/Databricks.
  • Contribute to data quality checks, monitoring, alerting, and documentation.
  • Support production operations: investigate issues, perform root-cause analysis, and implement fixes.
  • Create reusable components and follow coding standards, version control (Git), and CI/CD practices.
  • Collaborate with analytics teams to deliver curated datasets for dashboards and advanced analytics.
  • Follow security, privacy, and compliance best practices to protect sensitive data.
  • Stay curious—learn new tools and propose improvements to performance, cost, and reliability.

Required Qualifications

  • 1–3 years of hands-on experience in data engineering or closely related roles;
  • OR strong internships/capstone projects demonstrating practical data engineering skills (Exceptional fresh graduates are welcome to apply).
  • Proficiency in SQL and at least one programming language (Python, Java, or Scala).
  • Familiarity with distributed data processing (e.g., Apache Spark) and data lake/warehouse concepts.
  • Experience with relational databases and basic data modeling (star schema, normalization).
  • Understanding of ETL/ELT workflows and orchestration concepts (e.g., Airflow/Prefect).
  • Comfort with Git and collaborative development workflows.
  • Clear communication, growth mindset, and attention to detail.

Nice to Have

  • Exposure to Databricks, dbt, or cloud warehouse technologies (e.g., BigQuery, Redshift, Snowflake).
  • Experience with finance/ERP data (e.g., SAP FI/CO, SD, MM) and basic understanding of GL, AP/AR, Cost/Profit Centers.
  • Familiarity with data quality frameworks, testing (e.g., Great Expectations), and observability/monitoring tools.
  • Basics of containerization and CI/CD (Docker, GitHub Actions) and data security best practices.

What You'll Gain

  • Mentorship from senior data engineers and exposure to end-to-end data platform work.
  • Opportunities to ship production features, learn Databricks/Spark at scale, and develop your career path in Data Engineering.

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