Data Services

Data Engineering Outsourcing for Pipelines That Hold Up

ETL, ELT, real-time streaming, schema design, and orchestration built on dbt, Airflow, Kafka, and your cloud warehouse of choice.

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Overview

Pipelines that hold up through volume, schema drift, and scale

Our data engineering outsourcing practice designs pipelines, models the data, and orchestrates the flows that feed your warehouse, BI, and AI layers. We ship on Snowflake, Databricks, BigQuery, or Redshift with dbt, Airflow, Kafka, and Kinesis, covering ETL, ELT, real-time streaming, schema design, and orchestration. Tested, monitored, documented, and owned, with data engineering as a service or as a dedicated data engineering consulting company.

Years in business: 12
Team members: 65+
Global clients: 30+
Yr avg. client retention: 4+

Who this is for

Teams whose pipelines break every Monday morning and no one knows why.
Organizations that bought a warehouse and need someone to fill it with reliable, modeled data.
Companies adding sources faster than the in-house team can pipe them in.
Engineering leaders who need real-time streaming for product analytics, fraud, or operations.

What you get

Pipeline architecture - source-by-source map of ingestion method, refresh cadence, SLAs, and failure handling.
ETL/ELT pipelines - built in dbt, Airflow, or your existing orchestrator. Modular, tested, versioned in git.
Real-time streaming - Kafka, Kinesis, or Pub/Sub when latency matters.
Schema and model design - staging, intermediate, and mart layers with clear lineage from source to dashboard.
Data validation and quality - tests on row counts, nulls, uniqueness, referential integrity, and business rules with alerts on failure.
Documentation and runbooks - every pipeline owned, monitored, and recoverable.

How we work

01 Step

Audit

Inventory pipelines, sources, failure modes, downstream consumers, and SLAs.
02 Step

Plan

Architect the target pipeline graph, pick orchestrator, define schema, and set SLA tiers per pipeline.
03 Step

Build

Ship pipelines source by source. Each pipeline includes tests, monitoring, and documentation before it goes live.
04 Step

Test

Data parity checks against legacy sources, automated tests in CI, and stakeholder UAT.
05 Step

Scale

Add sources, layer streaming where needed, and harden orchestration as workload grows.

Tools & stacks we use

The platforms our team is fluent in for this practice. Most engagements span a few of these, picked for the actual problem rather than for the demo.

Fivetran
Stitch Data
Celigo
Azure Data Factory
Python
Spark
Apache Kafka
AWS Kinesis
GCP Pub/Sub
dbt Cloud
dbt Core
Apache Airflow
Prefect
Dagster
Snowflake
BigQuery
Redshift
Databricks
Great Expectations

Need dedicated experts?

Hire a specialist embedded with your team

Pre-vetted senior talent for this practice - hourly, retainer, dedicated FTE, or Micro-GCC. Vetted in 48 hours, managed end-to-end by H4H operations.

Data Engineer Hire a Data Engineer ETL Developer Hire a ETL Developer

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Frequently asked questions

Still have a question? Talk to a real human on our team - we usually reply within one business day.

Talk to a specialist

What does data engineering outsourcing mean in practice?

You hand off pipeline design, build, and ongoing maintenance to a named offshore team that ships in your stack, in your repos, on your cadence. Outsourced, not detached.

ETL vs ELT - which one should I pick?

ELT (load-then-transform) is the modern default for cloud warehouses because compute is cheap and stateful transforms in SQL are easier to test. ETL still applies when the source system needs heavy pre-processing or compliance scrubbing before landing.

How long does a typical engagement take?

A net-new pipeline stack runs 4-10 weeks depending on source count. Adding sources on a healthy foundation runs in days, not weeks.

How much does data engineering outsourcing cost?

Retainer engagements typically start mid-five figures monthly for a small offshore team. Project-based and dedicated FTE quoted separately.

What results can I expect?

Reliable refreshes, fewer Monday-morning fires, and a data layer that downstream BI and AI can actually trust. We have built the pipeline foundation under Bouqs' forecasting model and LegalZoom's BU-wide dashboards.

Can you work with our existing orchestrator?

Yes. We meet stacks where they are. We have shipped in Airflow, Prefect, Dagster, dbt Cloud, and custom orchestration.

How do you handle quality and monitoring?

Every pipeline ships with tests, monitoring, and alerting. Validation runs cover row counts, nulls, uniqueness, referential integrity, and business rules. Failures alert via Slack, email, or your incident tool.

How is H4H different from a freelance data engineer?

A freelancer ships one pipeline. We ship an architecture and the team to maintain it after.

Proof points

Related case studies

What we have shipped for clients with adjacent problems. Each one is sourced and attributable.

LegalZoom - Professional Services / LegalTech

Company-wide adoption from execs to department leads

Built data warehouse, pipelines, and a Tableau visualization layer balancing performance with drill-down to grain-level data.

Read case study

CandyClub - D2C / Subscription

Subscription data architecture enabling rotation, churn, and product-mix analytics

End-to-end ingestion, validation, and modeling for a curated subscription brand. Pipelines surfaced rotation, churn, and product-mix patterns the prior stack could not.

Read case study

Related services

Ready to put your data to work?

Book a free audit and we will map the problem, the metrics, and the smallest first build that proves value.

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