Data Engineering & Analytics Development Services

Data pipeline and ETL projects typically range from $30,000 to $300,000 depending on source system count and transformation complexity. Full data warehouse modernization projects range from $80,000 to $600,000+. We scope engagements in phases so pipeline value can be validated before the full platform is built.

A focused pipeline connecting 2-4 source systems typically takes 8-12 weeks. Full data warehouse or lakehouse platforms with governance and BI layers run 16-24 weeks. Timeline depends heavily on source data quality and how many systems need integration.

Most production systems need both, integrated as a lakehouse — structured data for BI reporting and raw data for exploratory analytics and ML training. We assess your actual query patterns during discovery rather than defaulting to one architecture.

It depends on the use case. Fraud detection, inventory sync, and operational monitoring typically need real-time or near-real-time pipelines. Financial reporting and historical analytics are usually well served by batch processing, which is simpler and cheaper to operate. We scope this per use case, not as a blanket architecture decision.

Yes. We prefer building on existing infrastructure where the foundation is sound — full rebuilds are rarely necessary. Discovery includes a data quality assessment identifying what needs remediation versus what can be built upon directly.

Access controls, audit logging, and data lineage documentation are designed into the pipeline architecture from the start, not added after a compliance review flags a gap. We've delivered HIPAA-compliant clinical data platforms and financial services data infrastructure subject to regulatory audit requirements.

It depends on team preference and existing infrastructure. Airflow has the largest ecosystem and community; Prefect offers a more modern developer experience; Dagster is strongest for teams that want asset-based data lineage built in. We recommend based on your team's existing skills and the complexity of your pipeline dependencies.

For standard SaaS source connectors (Salesforce, Stripe, HubSpot), commercial tools are often faster and cheaper than custom development. Custom pipelines earn their cost for proprietary systems, complex transformations, or where data volume makes per-row commercial pricing uneconomical. We often recommend a hybrid approach.

Budget 10-20% of initial build cost annually for maintenance, monitoring, and adaptation to upstream schema changes. Pipelines built without proper orchestration and monitoring cost significantly more to maintain, since failures surface as downstream reporting errors rather than pipeline alerts.

Both. We build the dashboard and reporting layer on top of the validated data pipeline, since a dashboard is only as trustworthy as the data feeding it — building them separately from different teams is a common source of dashboards nobody trusts.

Both. Startups typically start with a focused pipeline connecting 2-3 core systems and one reporting layer. Enterprise engagements add governance, multi-source integration, and compliance architecture — the same engineering standards apply to both, scoped to what's actually needed at each stage.

Every engagement starts under mutual NDA before any data schema, volume, or infrastructure details are shared. Discovery is a structured, time-boxed data quality and architecture assessment that produces a scoped proposal, not an open-ended consulting engagement.