Software Engineer

Revalue

Indonesia

On-site

IDR 450,000,000 - 900,000,000

Full time

6 days ago

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Benefits offered by this job

Meaningful equity

Flexible working environment

Parental leave benefits

Wellbeing support / therapy access

Supportive culture

Job summary

Revalue seeks a Software Engineer to join Platform Intelligence, turning analytical work into production-grade systems. You will build geospatial data pipelines, productionise ML workflows, and ensure reproducibility and observability across cloud environments.

The role sits at the boundary of software engineering, data science, and geospatial engineering, collaborating with domain experts to define what to build and how to model it.

Qualifications

Strong software engineering background with design, interface, and data modelling.
Scientific programming background in geospatial, ML, or similar domains.
Ability to translate messy data into correct representations and transformations.
Comfortable across stacks and tools; able to learn new languages quickly.
Experience with cloud data systems and modern product engineering practices.

Responsibilities

Make design and data-modelling calls for manifests and boundaries.
Build and maintain geospatial data pipelines and validated datasets.
Turn notebooks and prototypes into production-grade, modular code.
Productionise modelling pipelines with scheduling and concurrency.
Ensure runs are reproducible and observable with versioning and monitoring.
Develop data and monitoring products with proper cataloging.
Work on backend (Kotlin/Spring, Postgres) and frontend (React/Remix).
Own cloud infrastructure in Terraform across GCP and AWS.
Run discovery sessions with scientists and analysts.
Contribute to dev tooling and repo instructions.

Skills

Software engineering

Geospatial data

Python

Cloud platforms

Communication

SWE practices

Tools

Docker

GitHub Actions

Dagster

Terraform

Postgres

DuckDB

Dask

xarray

rasterio

geopandas

Job description

**Candidates must reside in Indonesia and have existing authorisation to work there**

About Us

Revalue is a carbon credit development platform focused on one of the planet's most critical but complex challenges - seeing nature regenerated at a planetary scale.

Our mission is to design the most cutting-edge and trusted nature credits for our planet.

We are bringing together a world-class team - forestry experts, ecologists, applied AI scientists, software engineers, geospatial analysts, commercial specialists, economists, and more - to create the unique DNA required to reimagine nature credits.

About the role

We are looking for a Software Engineer to join our Engineering team within Platform Intelligence.

Platform Intelligence develops the data infrastructure, machine learning, geospatial pipelines, and technical systems behind Revalue's nature and carbon intelligence products. We turn scientific and analytical work into repeatable, reliable outputs: ingesting and validating Earth observation datasets, producing the analyses and baselines that underpin our carbon methodologies, designing monitoring systems, and running the cloud platform underneath.

This role is about moving from analytical and ML prototypes to production-grade systems. You will work closely with engineers, data scientists, and analysts to build data pipelines, productionise models, and improve reproducibility, making workflows maintainable, observable, and usable by others.

Some of the most important work is deciding what to build, how to model it, and where to draw boundaries. You will work with domain experts to make those decisions rather than simply implementing predefined specifications.

It is a hands-on role at the boundary between software engineering, data science, geospatial engineering, and cloud infrastructure. You will not be expected to lead scientific model development, but you will make sure the models run reliably in production.

Who we are looking for

Please note: Candidates must have existing authorisation to work in Indonesia.

A strong mid-level or senior individual contributor who can own delivery independently and turn complex technical workflows into stable production systems.

Key skills:

Strong software engineering background. Design, interface, contract, data and domain modeling.
Scientific programming background in any domain: geospatial, remote sensing, ML, signal/audio/vision, quant, simulation, bioinformatics. We care more about the pattern of experience rather than the specific domain: translating messy, abstract, real-world structures into correct data representations and transformations.
Comfortable across stacks and tools: generalist disposition, able to pick up new languages/frameworks as our needs shift.
Familiar with modern SWE practices and product-engineering: discovery, scoping, iterative and incremental delivery, comfortable operating in ambiguity.

Hard skills:

Good engineering practice: testing, code review, CI/CD, modular design, documentation.
Experience productionising analytical, data science, or ML workflows: turning exploratory code into reliable systems, with a working understanding of batch processing, reproducibility, monitoring, and deployment.
Day-to-day work is in scientific Python. However, we weigh fundamentals and data aptitude above prior Python experience: if you have done this kind of work in C++, Scala, ... , that counts.
Cloud engineering experience, ideally on GCP and AWS, including designing and operating cloud-based data systems.
A pragmatic mindset: knowing when to build carefully, when to simplify, and when to avoid unnecessary complexity.

Soft skills:

Comfortable with high-visibility, shared-ownership, collaborative work: pairing on decisions, small trunk-based commits, and real-time feedback on work in progress.
Comfortable running discovery sessions with domain experts before building.
Strong communication, including presenting technical solutions to colleagues and non-technical stakeholders.
Advanced proficiency in written and spoken English.

Nice to have:

Experience in any of these would be valuable, though we do not expect all of them:

Geospatial data, raster/vector processing, Earth observation, or remote sensing.
Data quality checks, lineage, observability, or reproducibility tooling.
Full-stack experience: a backend and frontend you have maintained or owned.
Applying AI tools to improve engineering, developer, or analytical workflows.
Tools such as Docker, GitHub Actions, Dagster, Terraform, Postgres, DuckDB, Dask, xarray, rasterio, or geopandas.
Scientific computing, climate, nature, carbon, or environmental data.
Improving an existing platform, on a team where data scientists and analysts contribute code.

What you will do

Make design and data-modelling calls: what a manifest has to guarantee, where a boundary sits, what belongs in a shared library and what stays out, when to defer rather than build.
Build and maintain the geospatial data pipelines behind our datasets: syncing sources, validating and ingesting them, and exporting project-aligned outputs for analysis.
Turn notebooks, prototypes, and research code into tested, modular production code, and keep it that way as requirements change.
Productionise our modelling pipelines and the orchestration they run on: partitioned assets, schedules, and concurrency.
Make runs reproducible and observable: manifests, pinned dataset versions, deterministic outputs, and monitoring that tells us when something breaks.
Build new data and monitoring products, including proper cataloging for our datasets.
Performance tuning: profiling real runs, concurrency within memory bounds, and keeping runtime/cost predictable.
Work on the backend (Kotlin/Spring, Postgres) and frontend (React/Remix) where results reach customers.
Own cloud infrastructure in Terraform across GCP and AWS: batch jobs, storage, IAM, CI/CD, and alerting.
Run discovery with scientists and analysts.
Contribute to dev tooling: ship/release scripts, agent skills that encode our engineering procedures, and repo instructions.

What success looks like

In the first few months, you will get to know our platform, cloud setup, and data workflows, and start owning productionisation work. Over time you will help build clearer patterns, more reliable pipelines, and better reproducibility and monitoring.

You will be doing well if analyses are easier to run and repeat, if analysts and scientists depend less on manual processes, if pipelines are predictable in runtime and cost, and if fewer critical systems rely on one person to keep them moving.

What We Offer