Data Pipeline Engineering

From Data Chaos to Clean Intelligence

Your data is scattered across 15+ systems, trapped in silos, and too messy for AI. We build unified data pipelines that extract, transform, and deliver clean, reliable data—making your AI and analytics actually work.

70%+ reduction in data errors
80% faster analytics
100% audit compliance

Your Data Infrastructure Is Holding You Back

let's be honest about your data situation:

Data Is Scattered Everywhere

Salesforce, NetSuite, Google Analytics, Stripe, Zendesk, spreadsheets, legacy databases... nobody has a complete view of the business.

Analysts Waste 80% on Wrangling

Instead of generating insights, your team spends their time downloading exports, copy-pasting between spreadsheets, and cleaning data.

Data Quality Is Terrible

30% duplicate records, inconsistent formats, missing fields, outdated information. Finance, Sales, and Accounting show different revenue numbers.

AI Projects Fail Due to Data

You've tried ML models and BI dashboards, but can't get clean training data. Projects die in 'pilot purgatory' or show conflicting numbers.

The Real Business Impact:

Current State

  • • 3 analysts spend 25 hrs/week on data wrangling
  • • 2-3 week lag to answer executive questions
  • • AI projects stalled 6+ months on data prep
  • • $200K/year wasted on manual data work

Plus: Opportunity cost of delayed decisions

With Unified Data Platform

  • • Analysts spend 90% on insights, not wrangling
  • • Real-time dashboards, questions answered in minutes
  • • AI/ML projects have clean data from day one
  • • Single source of truth everyone trusts

Result: Faster decisions, lower costs, AI-ready

Unified Data Infrastructure Built for Scale

We build production-grade data pipelines that eliminate the chaos:

1

Extract: Connect to Everything

Pull data from all your sources automatically

  • SaaS Apps: Salesforce, HubSpot, Stripe, Zendesk, Shopify, QuickBooks, Google Analytics, Mixpanel
  • Databases: PostgreSQL, MySQL, MongoDB, SQL Server, AWS RDS, DynamoDB
  • Files & Legacy: CSV/Excel from SFTP, S3, email attachments, on-premise systems, mainframes
  • Streaming: Real-time events, Kafka, webhooks, change data capture (CDC)
2

Transform: Clean and Standardize

Make raw data analytics-ready

  • Data Cleaning: Remove duplicates (fuzzy matching), fix formatting, handle nulls, standardize values
  • Data Enrichment: Geocoding, company info, categorization, derived metrics (LTV, churn risk)
  • Data Modeling: Unified customer view, star schema for analytics, aggregations, slowly changing dimensions
  • Business Logic: Your MRR/ARR definitions, revenue recognition rules, custom metrics
3

Load: Deliver to Your Warehouse

All clean data in one unified warehouse

  • Warehouse Options: Snowflake, Google BigQuery, AWS Redshift, Azure Synapse, PostgreSQL
  • Data Organization: Raw layer (exact copy), staging (cleaned), analytics (business-ready), department marts
  • Benefits: Single source of truth, fast queries, historical tracking, scalable from GB to PB
  • Security: Encryption, access controls, audit logs, cost-effective pay-for-use model
4

Monitor: Ensure Data Quality

Continuous monitoring and alerting

  • Quality Checks: Freshness ('data hasn't updated in 6 hours'), volume anomalies, schema drift, value validation
  • Alerting: Slack/email when checks fail, severity levels, automatic retries for transient failures
  • Observability: Data lineage (trace source → report), impact analysis, SLA monitoring
  • Governance: Version control for all transformations, access controls, compliance audit trails

THE EDGEFIRM DIFFERENCE

Unlike DIY with Fivetran/dbt:

  • • We handle the complex sources
  • • Custom transformations for your logic
  • • Production-grade monitoring included

Unlike large consultancies:

  • • 4-5 month delivery (not 12-18)
  • • Senior engineers, not juniors
  • • Fixed pricing, you own the code

Unlike managed platforms:

  • • No per-row/per-user fees
  • • Works in your cloud account
  • • Full control and portability

Built on Modern Data Stack

Ingestion & Orchestration

  • Airbyte
  • Fivetran
  • Apache Airflow
  • Prefect
  • Dagster

Transformation

  • dbt (data build tool)
  • Great Expectations
  • Python/pandas
  • Apache Spark
  • SQL

Storage & Warehousing

  • Snowflake
  • Google BigQuery
  • AWS Redshift
  • PostgreSQL
  • Delta Lake

Monitoring & Quality

  • Monte Carlo
  • dbt tests
  • Custom alerts
  • DataDog
  • CloudWatch

Data Pipelines for Every Industry

E-Commerce & Retail

Unified Customer View, Inventory Sync, Marketing Attribution

Challenges

  • Customer data fragmented across Shopify, email, ads, and support
  • Inventory out of sync between warehouse, stores, and marketplace
  • Can't attribute sales to marketing campaigns accurately

Our Solutions

  • Unified customer 360: merge transactions, browsing, support, email engagement
  • Real-time inventory sync across all channels with auto-reorder triggers
  • Multi-touch attribution model connecting ad spend to actual revenue

Results

  • 360° customer view across all touchpoints
  • 95% inventory accuracy (was 70%)
  • 20% improvement in marketing ROI

How We Build Data Pipelines in 4-5 Months

Month 1

Discovery & Architecture

  • Interview stakeholders and document data pain points
  • Inventory all data sources: volume, quality, update frequency
  • Design target data architecture and warehouse schema
  • Build ROI model and prioritize data sources by impact
  • Set up development environment and tooling

Deliverable: Technical architecture document, project roadmap, infrastructure setup

Month 2

Data Pipeline Development

  • Connect to top 5-10 priority data sources
  • Build extraction pipelines with incremental loading
  • Set up data warehouse and raw data landing zones
  • Implement initial data quality checks
  • Test data freshness and completeness

Deliverable: Data flowing from priority sources into warehouse

Month 3

Transformation & Quality

  • Build dbt transformation models for business logic
  • Create unified data models (customer 360, product, finance)
  • Implement comprehensive data quality framework
  • Set up alerting for quality issues and pipeline failures
  • Document data dictionary and lineage

Deliverable: Clean, modeled data ready for analytics

Month 4

Integration & Testing

  • Connect BI tools and build initial dashboards
  • Set up reverse ETL to operational systems if needed
  • Performance optimization and cost tuning
  • User acceptance testing with analytics team
  • Add remaining data sources

Deliverable: End-to-end pipeline with BI integration

Month 5

Launch & Documentation

  • Production deployment with monitoring
  • Train your team on pipeline management
  • Complete documentation: architecture, runbooks, data dictionary
  • 30 days post-launch support and optimization
  • Knowledge transfer and handoff

Deliverable: Production data platform with trained team

Transparent Pricing for Data Pipelines

Typical Investment Range

$50,000 - $150,000

Full project delivery in 4-5 months

Factors that affect pricing:

Number of Sources

5-10 sources vs 20+ systems to connect

Data Volume & Velocity

GB vs TB, batch vs real-time requirements

Transformation Complexity

Simple joins vs complex business logic and ML features

Compliance Requirements

PII handling, HIPAA, SOC 2, data residency needs

what's Included:

Complete discovery and architecture design
Data pipeline development (ETL/ELT)
Data warehouse setup and optimization
Transformation models (dbt or equivalent)
Data quality monitoring framework
BI tool integration
Documentation and data dictionary
Team training and knowledge transfer
30 days post-launch support

Common Questions About Data Pipelines

Fivetran and Airbyte are great for extraction (the 'E' in ETL), and we often use them. But they don't solve the hard problems: data modeling (how do you calculate MRR?), quality monitoring (is the data correct?), transformation logic (business rules), and integration with your analytics tools. We build the complete data platform, not just the connectors. We also handle sources these tools don't support and build custom transformations for your specific business logic.

Almost anything. SaaS applications (Salesforce, HubSpot, Shopify, Stripe, etc.), databases (PostgreSQL, MySQL, MongoDB, SQL Server, Oracle), files (CSV, Excel, JSON from SFTP, S3, email), streaming data (Kafka, webhooks, CDC), and even legacy systems like mainframes and on-premise databases behind firewalls. If it has an API or can export data, we can integrate it.

Data quality is built into every layer. During ingestion: Schema validation, freshness checks, row count monitoring. During transformation: Deduplication, standardization, null handling, business rule validation. Post-load: Automated testing, anomaly detection, data profiling. We use Great Expectations and dbt tests to catch issues before they reach dashboards. You get Slack alerts when something's wrong, and dashboards showing data health metrics.

Yes. If you already have Snowflake, BigQuery, Redshift, or another warehouse, we build on top of it. We'll assess your current setup, recommend improvements, and integrate new pipelines alongside existing ones. We can also help migrate from one warehouse to another if needed. Our transformations are portable SQL/dbt, so you're not locked into any vendor.

We support multiple latency tiers. Batch (hourly/daily) for most analytics use cases—simplest and cheapest. Near real-time (5-15 minutes) using streaming ingestion and micro-batching. True real-time (seconds) using Kafka, change data capture (CDC), and streaming transformations. Most clients find that near real-time is sufficient—only a few metrics truly need sub-minute latency. We'll help you determine what's actually needed vs. nice-to-have.

Security is built-in from day one. Infrastructure: Encryption at rest and in transit, VPC isolation, IAM roles with least privilege. Access Control: Role-based access, column-level security for PII, row-level security for multi-tenant data. Audit: Complete logging of who accessed what, data lineage for compliance. Compliance: SOC 2 aligned, HIPAA compliant deployments, GDPR-ready with data locality and deletion. We work within your security requirements and can deploy in your cloud account.

We build for minimal maintenance. Pipelines are self-healing with automatic retries. Schema drift detection catches source changes before they break things. Alerting notifies you only when human intervention is needed. Typical ongoing work: Adding new data sources (we document how, or you can engage us). Updating transformations when business logic changes. Responding to alerts (most are auto-resolved). Most clients manage this with existing team, or we offer retainer support ($5K-15K/month) for hands-off operation.

Complement Data Pipelines With:

SERVICE OVERVIEW

Service Type

Data Engineering

Timeline

4-5 months

Investment

$50K - $150K

ROI Timeline

6-12 months

KEY BENEFITS

  • Single source of truth for all data
  • 80% less time on data wrangling
  • AI-ready, clean datasets
  • Full audit trail and compliance
  • Scalable from GB to PB

TYPICAL RESULTS

70%

reduction in data errors

80%

faster analytics delivery

$500K+

annual savings

Ready to Transform Your Business with AI Solutions?

Schedule a free strategy call to discuss your project and get a custom AI implementation roadmap.

50+
Projects Delivered
100%
Client Satisfaction
60-80%
Cost Reduction
3-5mo
Implementation Time

Or email us directly at hello@edgefirm.io. We typically respond within 2 hours during business days.