Data Engineer

Love, Bonito; Jakarta Selatan; On-site; IDR 15.000.000 - 25.000.000; Full time

Job summary

A leading womenswear brand in Jakarta Selatan is seeking a Data Engineer to enhance data infrastructure. The role includes pipeline optimisation, data quality assurance, and collaboration with analytics teams. Ideal candidates have a Bachelor's degree, 2-4 years of experience, and strong skills in Python and SQL. This position offers opportunities for growth in a dynamic environment.

Qualifications

• 2-4 years data engineering experience.
• Proven track record of building production data pipelines at scale.
• Experience with cloud data warehouses like Redshift or BigQuery.

Responsibilities

• Update documentation of existing data pipelines.
• Refactor legacy code for improved scalability and maintainability.
• Implement comprehensive data quality checks within pipelines.
• Troubleshoot pipeline failures and conduct root cause analysis.

Skills

Data Warehousing

Python

SQL

Problem-solving

Communication skills

Education

Bachelor's degree in Computer Science or related field

Tools

AWS

Docker

Kubernetes

Job description

About us

Love, Bonito is Southeast Asia’s leading womenswear brand, built with a mission to empower women to find confidence through style. Incorporated in 2010 in Singapore, we’ve since grown from a humble online startup into a multi‑channel business with over 20 stores and counting across Asia and a thriving online presence.

For the next decade, we are looking to expand through new categories, markets, wholesale partnerships, and brand acquisitions. We are evolving into a next‑generation regional consumer group — one that blends heart with performance, and creativity with technology.

At our core, we’re a team of builders, dreamers, and doers who believe culture is more than words on a wall — it’s how we show up every day. We move fast, stay curious, and take bold bets on ideas and people we believe in. Here, you’ll find a community that challenges you to grow, trusts you to lead, and celebrates you for being you. Together, we’re shaping the future of Asian brands — from right here in Southeast Asia, to the world.

Why join us?

At Love, Bonito, you’ll do more than just a job — you’ll help build a movement. We’re creating an organization that’s lean, bold, and full of heart — where every person has the space to make an impact. Here’s what you can expect:

• Purpose with performance: We’re building a world‑class Asian brand that competes globally. We hold ourselves to high standards and operate with purpose, integrity, grit and excellence. Join us if you take customer service excellence seriously and are passionate about creating real impact!
• Growth that’s real: We are known to be incredibly dynamic and fast‑paced. You’ll be expected to learn fast, stretch beyond your comfort zone, and work alongside people who challenge and support you in equal measure. Don’t join us if you’re looking for a comfortable, fully structured setup, but do join us if you’re excited to build, shape, and make an impact together!
• Culture at our core: If you’re looking for a no‑corporate‑BS environment, you’ll fit right in. We lead with empathy, celebrate individuality, and believe that great work comes from trust, not titles. We believe in teamwork and effective collaboration - because when we run together, we go further. If this speaks to your values, come join us and be part of #TeamLB!

Join us as we redefine what it means to build an enduring global consumer group.

The role

We’re seeking a Data Engineer to enhance the reliability, efficiency, and documentation of our data infrastructure. This role focuses on pipeline optimisation, data quality assurance, and maintaining robust data operations that support analytics, reporting, and machine learning initiatives.

Main responsibilities

Pipeline Development & Optimisation

• Update documentation of existing data pipelines to establish clear operational standards and knowledge transfer
• Refactor legacy code to improve scalability, performance and maintainability
• Optimise aggregation layers and transformation logic for reporting workloads
• Reduce pipeline runtimes through strategic improvements in query optimisation, partitioning and resource allocation

Data Architecture & Modelling

• Review and enhance data models supporting analytics, reporting and machine learning use cases
• Design and implement efficient data structures that balance query performance with storage costs
• Collaborate with analytics and data science teams to ensure data models meet downstream requirements

Data Quality & Reliability

• Implement comprehensive data quality checks and validation frameworks within pipelines
• Develop unit tests for data transformations to ensure correctness and prevent regressions
• Monitor data integrity throughout the pipeline lifecycle
• Establish and maintain data quality metrics and alerting mechanisms

Operations & Incident Management

• Troubleshoot pipeline failures and implement root cause analysis
• Design and deploy preventative measures to minimise future incidents
• Maintain pipeline uptime targets of 99%
• Ensure SLA adherence across critical data workflows

Governance & Security

• Maintain data security, governance and compliance standards across all data assets
• Implement access controls and data lineage tracking
• Ensure adherence to regulatory requirements and internal data policies

Qualifications

• Bachelor’s degree in Computer Science, Engineering, or a related field.
• 2‑4 years data engineering experience
• Proven track record of building and maintaining production data pipelines at scale
• Strong problem‑solving skills with focus on systematic root cause analysis
• Excellent communication skills for technical documentation and cross‑functional collaboration

Required Technical Skills

• Core Stack
  • Data Warehousing: Experience with cloud data warehouses (Redshift, BigQuery) or lakehouse architectures
  • AWS Cloud Services: Working knowledge of S3, IAM, EC2 and related services
  • Programming: Strong Python skills with focus on PySpark, SQL, Scala for ETL
• DevOps & Engineer Practices
  • Containerisation (Docker, Kubernetes)
  • CI/CD pipelines and version control (Git, GitHub Actions)
• Preferred Qualifications
  • Experience optimisation of aggregation layers for high‑volume reporting workloads
  • Familiarity with Delta Lake and lakehouse design patterns
  • Background in distributed computing and Spark performance tuning
  • Infrastructure as code and automated deployment practices