Seattle Software Agency SeattleSoftware Agency

Custom Data Pipeline & ETL Development

Your data lives in 15 different systems. Build the pipelines that bring it all together.

Every growing company hits the same data problem: critical business data scattered across CRM, accounting, marketing tools, product databases, and spreadsheets. Getting a single source of truth requires ETL (Extract, Transform, Load) pipelines that reliably move and transform data between systems.

Tools like Fivetran and Stitch handle basic database replication well. But when you need custom transformations, complex business logic in the pipeline, real-time sync between specific systems, or data quality validation — off-the-shelf ETL tools hit their limits.

We build custom data pipelines for companies consolidating data for analytics, businesses needing real-time sync between operational systems, organizations with data quality problems that require custom validation, and teams whose data transformation logic is too complex for visual ETL builders.

What You Get

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Custom Extraction

Pull data from any source: APIs, databases, files, webhooks, or legacy systems with custom protocols.

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Business Logic Transforms

Apply your specific business rules during transformation: deduplication, enrichment, normalization, and calculated fields.

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Data Warehouse Loading

Load into your data warehouse (BigQuery, Snowflake, Redshift, PostgreSQL) with proper schema management and incremental updates.

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Data Quality Validation

Automated quality checks: schema validation, completeness verification, anomaly detection, and data freshness monitoring.

Real-Time & Batch

Real-time streaming for operational data, batch processing for analytics. Choose the right pattern for each pipeline.

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Pipeline Monitoring

Dashboards showing pipeline health, data freshness, error rates, and processing latency. Alerts when things go wrong.

When Custom Pipelines Make Sense

For basic database replication (PostgreSQL to BigQuery, Salesforce to Snowflake), Fivetran or Airbyte is probably the right choice. Custom data pipelines are for scenarios where the transformation logic, data quality requirements, or integration complexity exceeds what SaaS ETL tools can handle.

Signs you need custom pipelines: your data transformations require complex business logic that visual tools can't express, you need real-time sync between specific systems (not just replication), your data quality problems require custom validation and cleaning, or you're spending more on Fivetran than you would on purpose-built infrastructure.

The Modern Data Stack

We build data pipelines that fit into the modern data stack: extraction from source systems, loading into a cloud data warehouse, and transformation using dbt or custom SQL for analytics-ready data.

For operational data needs (real-time sync between systems), we build event-driven architectures using message queues (Redis, RabbitMQ, Kafka) that process and route data as it changes.

Technologies We Use

PythonNode.jsPostgreSQLBigQueryRedisDockerAirflow

Frequently Asked Questions

How much do custom data pipelines cost?
Simple pipelines connecting a few systems start around $15,000. Complex data infrastructure with multiple sources, real-time processing, and data quality frameworks ranges from $40,000 to $100,000.
How long does it take?
A single pipeline (source to warehouse) takes 2-4 weeks. A comprehensive data infrastructure project connecting multiple systems takes 8-16 weeks.
What about Fivetran or Airbyte?
We use Fivetran or Airbyte for standard connectors where they work well. Custom pipelines are for the sources and transformations these tools can't handle. A hybrid approach is common.
Do you build the data warehouse too?
Yes. We set up and configure your data warehouse (BigQuery, Snowflake, or PostgreSQL), design the schema, and build the dbt models that transform raw data into analytics-ready tables.

Data Scattered Across Too Many Systems?

Book a free consultation. We'll map your data sources and show you what a reliable data pipeline infrastructure could look like.

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