Data Platform Engineering

Data infrastructure built to be trusted, not just deployed

Design databases, streaming and analytics platforms for the recovery, visibility and operating discipline that production data demands.

A backup is only useful after restore has been proved

A data platform earns trust during the events nobody wants: an accidental deletion, a pipeline silently falling behind, a schema change with unexpected impact or a workload that grows beyond its original assumptions. A successful job exit or a green backup badge does not prove that records are complete or recovery will meet the business need. Data Platform Engineering covers the operational database and streaming layer beneath products and business systems. We design and improve PostgreSQL, Kafka and ClickHouse platforms around actual workloads, recovery expectations and team ownership, then automate monitoring and routine operations so uncertainty becomes visible before an incident exposes it.

What we build and operate

Infrastructure choices follow data access, throughput, recovery and ownership requirements.

• Reliable PostgreSQL

  High availability, replication, PgBouncer connection pooling and query performance tuned against the application's workload.

• Kafka pipeline design

  Topics, partitions and consumer groups shaped around throughput and ordering, with consumer lag treated as an operating signal.

• ClickHouse analytics

  Schema, partitions and materialized views aligned with the queries dashboards and reports actually run.

• Tested backup and restore

  Automated backup schedules are paired with isolated restore drills and evidence that recovery objectives are realistic.

• Pipeline quality monitoring

  Freshness, volume and consistency checks catch pipelines that are technically running but functionally incomplete.

• Migration and replatforming

  Incremental transitions, validation and rollback plans reduce the risk of moving critical data or changing its operating model.

A safety-first engineering approach

• Assess

  Review workloads, incidents, performance, access, topology and the real backup and restore history.

• Design

  Choose configuration, indexes, partitions and services against observed query and throughput patterns.

• Implement

  Make changes incrementally with validation, backup coverage and an explicit rollback path.

• Operationalise

  Add high-signal monitoring, tested runbooks, ownership and capacity and recovery routines.

A successful job can still produce the wrong data

The dangerous data failure is often not a crash. A pipeline can return success while dropping a proportion of records, processing yesterday's partition or falling further behind its source. Monitoring only process uptime and exit codes misses these conditions. Freshness, expected volume, consumer lag and consistency checks provide a closer view of whether the data product is actually usable. Migration follows the same evidence-based approach. New and old paths may run together long enough to compare results, cutover conditions are written down, and rollback remains available until validation is complete. Recovery procedures are tested rather than assumed, with frequency matched to the system's criticality and the organisation's recovery objectives.

Data platform technology

• PostgreSQL
• Amazon RDS
• Cloud SQL
• Apache Kafka
• Amazon MSK
• Confluent Cloud
• ClickHouse
• PgBouncer
• Debezium
• Grafana

Related expertise

Combine this service with the capabilities needed for a complete, operable platform.

• Cloud Foundations

  Place data services inside a governed identity, network and recovery foundation.

• Observability & SRE

  Connect data-platform symptoms to service health, runbooks and incident response.

• CI/CD Modernization

  Apply repeatable validation and controlled promotion to schema and platform changes.

Data platform questions

• Can you help migrate from a database service we have outgrown?

  Yes. We plan migrations with validation and a tested rollback path, and may run old and new systems in parallel briefly where the application and consistency model permit it.

• How often should restores be tested?

  Frequency should follow business criticality and recovery objectives. Quarterly drills are a common baseline for important systems, while systems with lower tolerance for loss or delay may require more frequent automated validation.

• Can transactional and analytical systems work together?

  Yes. We can design the movement from PostgreSQL into ClickHouse or another analytical store using Kafka, Debezium or managed change-data-capture services without overloading the transactional database.

• What does pipeline reliability monitoring include?

  In addition to job health, it can include data freshness, expected volume, consumer lag, schema compatibility, error destinations and consistency checks appropriate to the data product.

Find out whether the platform will recover before an incident

Share the database, pipeline or migration concern that is difficult to prove today. We will propose a practical assessment path.