Remote · Contract · Project delivery · Consulting

DataOps engineering for high-throughput data systems

DataOps applies delivery, reliability and observability practices to data systems. Pipelines, schemas, Kafka, databases, processing workloads and platform changes need a controlled path to production. The work focuses on throughput, backpressure, data quality signals, recovery and the operational cost of running data at scale.

Discuss the project↗How delivery works↓

When this service helps

Problems worth solving.

Ingestion volume grows faster than capacity planning and production visibility.

Pipeline failures are detected late or cannot be traced across system boundaries.

Data services depend on manual deployment, risky schema changes or unclear recovery procedures.

Engagement outcomes

The outcome must be measurable.

↗

A mapped data path with explicit throughput, latency and recovery expectations.

↗

Observable ingestion, processing, storage and consumer health.

↗

Repeatable deployment and change processes for data workloads.

↗

Capacity and failure controls for backpressure, replay and degraded operation.

Delivery process

From discovery to stable operations.

01
Map the flow
Trace producers, ingestion, processing, storage and consumers with volume, latency and ownership.
02
Set signals
Define data and infrastructure health indicators for lag, errors, throughput, saturation and freshness.
03
Engineer reliability
Improve deployment, partitioning, backpressure, replay, schema change and recovery paths.
04
Operate at scale
Test capacity, document failure modes and use production evidence to tune performance and cost.

Delivery scope

What the team can put to work.

Possible deliverables

Data-flow and capacity model
Kafka and ingestion review
Pipeline deployment workflow
Data observability design
Recovery and replay runbooks
Performance and cost plan

Technology and environments

KafkaGoKafkaRabbitMQRedisPostgreSQLKubernetesVictoriaMetricsPrometheusGrafanaGo

Delivery evidence

Data systems measured in billions of records

SQM involved signaling-quality monitoring with a context capable of receiving roughly 500 billion new records per day. The work included Kubernetes-based DataOps processes, ingestion reliability and monitoring foundations for operating at that scale.

See projects and operating scale↗

FAQ

Common engagement questions

Does DataOps include database and Kafka operations?

Yes. The scope can include ingestion, Kafka, queues, databases, pipeline deployment, monitoring, capacity, recovery and the platform supporting those workloads.

Can an existing data platform be improved incrementally?

Yes. The highest-risk data path is mapped first, then observability, deployment and recovery gaps are improved without requiring an immediate full rewrite.

Is application development included?

When the data platform needs software, supporting services and automation can be built with Go, Python or other suitable tools as part of the delivery.

Direct contact

Clarify the problem. Then decide the right path.

For a remote role, contract, defined project or consultation, send a short description of the current situation and expected outcome.

sinamohsenifar@gmail.com WhatsApp Telegram