Question 1

How much do data engineering services cost?

Accepted Answer

Our data engineering projects start at $7,500. A focused pipeline connecting a few systems to a reporting dashboard sits at the lower end; a full data warehouse with modeled data across many sources runs $20,000-$50,000+. We scope after an audit of your current systems and always start with the highest-value slice rather than a boil-the-ocean platform build.

Question 2

What is data engineering, exactly?

Accepted Answer

Data engineering is the plumbing that makes your data usable: extracting it from the systems where it lives (CRM, ERP, app databases, spreadsheets), cleaning and combining it, and landing it somewhere analysts and dashboards can query reliably. Without it, "what were sales last quarter by region?" takes a week of spreadsheet surgery instead of five seconds.

Question 3

How is data engineering different from data science?

Accepted Answer

Data engineering builds the reliable foundation — pipelines, warehouses, quality checks. Data science and analytics sit on top, extracting insights, forecasts, and models from that foundation. Most companies that hire data scientists too early spend 80% of that expensive time doing data engineering. Foundation first.

Question 4

When does a business need data engineering?

Accepted Answer

Common signals: reporting takes days of manual spreadsheet work, numbers differ depending on who pulls them, your data lives in five disconnected tools, dashboards are stale or nonexistent, or you want to use AI on your business data. If two of those sound familiar, an initial audit will pay for itself.

Question 5

What is an ETL or data pipeline?

Accepted Answer

A pipeline automatically moves data from source systems into a central store on a schedule — extracting it, transforming it into clean consistent shapes, and loading it into a warehouse (ETL/ELT). Once built, the reports that took your team days assemble themselves overnight, every night.

Question 6

Which data warehouse should we use — Snowflake, BigQuery, or Postgres?

Accepted Answer

For small-to-mid data volumes, a well-modeled PostgreSQL warehouse is cheap and plenty fast. Snowflake and BigQuery earn their cost at larger scale or when many teams query concurrently. We pick based on your data volume, existing cloud, and budget — not on what is fashionable. Migrating later is straightforward when pipelines are built cleanly.

Question 7

What technologies do you work with?

Accepted Answer

Python and SQL at the core, with Airflow or Dagster for orchestration, dbt for transformations, and PostgreSQL, Snowflake, or BigQuery for warehousing. For streaming and larger workloads: Kafka and Spark. Dashboards land in tools your team will actually open — Metabase, Looker Studio, or Power BI.

Question 8

How long does a data project take?

Accepted Answer

A first useful pipeline with a live dashboard: 2-4 weeks. A modeled warehouse across several sources: 6-12 weeks. We deliver incrementally, so your first automated report arrives in weeks, not at the end of a long project.

Question 9

How do you handle data quality?

Accepted Answer

Automated tests at every stage: schema checks, freshness monitors, anomaly detection, and reconciliation against source systems. When something upstream breaks — a renamed column, an API change — you get an alert instead of a quietly wrong dashboard, which is the most dangerous failure mode in analytics.

Question 10

Can you make our data ready for AI?

Accepted Answer

Yes — AI initiatives live or die on data quality and accessibility. We build the clean, well-documented, queryable foundation that AI features need, whether that is feeding an LLM accurate business context, powering recommendations, or forecasting. This groundwork is exactly the same warehouse and pipeline work, done with AI use cases in mind.

Question 11

Who maintains the pipelines after launch?

Accepted Answer

Your project includes 3 months of support, and everything is documented and version-controlled so your team can own it — you have full code ownership. Most clients keep us on a light monthly retainer for monitoring, fixes when upstream systems change, and new data sources as the business grows.

Data Engineering

What We Deliver

Data Pipelines & ETL

Data Warehousing

Analytics & BI

Data Quality & Governance

Data Services

Data Integration

Reporting & Dashboards

AI-Ready Data

Our Process

Audit & Discovery

Architecture

Build Pipelines

Operate & Iterate

Technologies We Use