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📊 Modern Data Stack

Data Pipelines That
Actually Stay Reliable

Broken pipelines, stale dashboards and untrusted data are a tax on your entire organisation. ZippyOPS automates your data engineering workflows with quality gates, observability and modern stack best practices.

What We Do

We design and build modern data platforms with automation at every layer — ingestion, transformation, orchestration and serving — so your data teams spend time analysing data, not fixing broken pipelines.

  • Data pipeline orchestration with Apache Airflow, Prefect and Dagster
  • Data transformation and modelling with dbt — version-controlled, tested and documented
  • Data quality automation with Great Expectations and Soda
  • Streaming pipeline design with Kafka, Flink and Spark Streaming
  • Data platform architecture on Snowflake, BigQuery and Databricks
  • Data observability — pipeline health, freshness and quality monitoring
  • Data catalogue and lineage with OpenMetadata and DataHub
📊
Airflow
dbt
Kafka
Spark
Snowflake
BigQuery
Databricks
Great Expectations
Dagster
Prefect
Apache Iceberg
DuckDB
Soda
Flink
Delta Lake
Data pipeline reliability rate 99.2%

What You'll Walk Away With

A production-grade data platform with automated ingestion, transformation and quality checks

dbt transformation layer with tests, documentation and CI/CD — data as code

Data observability dashboard — pipeline health, freshness SLAs and quality scores

Self-service analytics layer enabling business teams to query data without engineering support

Ready to Fix Your Data Pipelines?

Book a free data platform review. We'll audit your current stack and design a reliable, automated data architecture for your organisation.

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