Service
Data Engineering with PT Cloud Platform Indonesia (PT CPI)
Your analytics and ML teams need trustworthy data at scale. We engineer batch and streaming platforms on BigQuery and Dataflow with modern Python tooling—not brittle scripts that only one person understands.
PT CPI builds reliable data pipelines on GCP with Python, Polars, Beam, Spark, dbt, and orchestration (Airflow, Dagster)—from ingestion and lakehouse patterns to production SLAs and data contracts.
Data engineering at PT CPI starts with clear contracts: schemas, freshness SLAs, ownership, and how downstream consumers (BI, ML, FinTech) depend on each dataset. We implement lakehouse and warehouse patterns on BigQuery with dbt for transformations and testing, and we use Polars and Python for high-performance local processing when it keeps pipelines simpler and cheaper.
For large-scale ingestion and stream processing we deploy Apache Beam on Dataflow, Spark where cluster economics fit, and reliable orchestration with Airflow or Dagster. Infrastructure is defined with Terraform and OpenTofu; secrets, IAM, and network paths follow the same landing-zone standards as your application estate.
Every pipeline ships with observability—data quality checks, lineage where required, and runbooks for backfill and incident response—so platform teams and auditors see the same facts about what ran, when, and with what outcome.
Who this is for
Data platform leads, analytics engineering teams, and enterprises centralizing event streams, core banking feeds, or product telemetry on Google Cloud.
What we deliver
- Polars and Python for fast, expressive ETL and data-quality workloads
- dbt models, tests, and documentation on BigQuery with CI/CD promotion
- Apache Beam on Dataflow and Spark for batch/stream at enterprise scale
- Airflow or Dagster orchestration, data contracts, and operational runbooks
How we engage
- Data discovery: sources, consumers, compliance constraints, and current pipeline pain points.
- Target architecture: storage layers, orchestration, IAM, and toolchain (dbt, Beam, Polars).
- Incremental build with measurable SLAs and stakeholder sign-off on critical datasets.
- Operate and improve: cost tuning, quality metrics, and handover to your platform team.
Related documentation
Open PT Cloud Platform Indonesia documentation →Related services
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