Data Engineering

ETL Pipeline Development That Works in Production From Day One

Numlytics builds production-grade ETL and ELT pipelines for enterprises across the US, UK, Australia & UAE. Azure Data Factory, Databricks, dbt, Apache Spark, and Airflow - delivered by certified data engineers with hands-on production experience. Every pipeline includes monitoring, error handling, and documentation. No rebuilds. No PoCs dressed up as deliverables.

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Production-ready from sprint 1 - monitoring, alerts & docs included
Certified on ADF, Databricks, dbt, Spark & Airflow
First pipeline delivered in 2 weeks from kickoff
Up to 50% lower cost vs US/UK data engineering firms
Delivery Facts
2wk
First pipeline live
within 2 weeks
0
Pipeline failures
at cutover - ever
30+
Production pipelines
delivered globally
50%
Lower cost vs US/UK
data engineering firms
We build with
Azure Data Factory
Databricks
Microsoft Fabric
dbt
Apache Spark
Apache Airflow
Snowflake
Fivetran
Apache Kafka
What We Build

Production-Grade Pipelines,
Not PoCs That Break on Monday

Most data teams have pipelines. What they don't have is confidence in them. Silent failures discovered at 9am by a business user who noticed the dashboard numbers haven't moved. SQL scripts running on someone's laptop that nobody else understands. Manual CSV uploads that should have been automated two years ago.

Our ETL pipeline development service builds the infrastructure that fixes this. Every pipeline we deliver is built to production standards from the first sprint - with automated monitoring, failure alerting, retry logic, incremental load patterns, and full documentation. Not a working prototype that needs to be rebuilt before it can be trusted.

We cover the full data pipeline stack - ingestion, transformation, orchestration, and quality validation, across Azure Data Factory, Databricks, dbt, Apache Spark, and Airflow, on your existing cloud platform.

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Why Clients Come to Us
"Our pipelines break every Monday morning"
Weekend batch runs fail silently. The first sign of a problem is an analyst noticing stale data at 9am. No monitoring, no alerts, no on-call process.
"We have 80 SQL scripts running on someone's laptop"
The entire data infrastructure depends on a single person's local environment. No version control, no orchestration, no documentation, and one resignation away from collapse.
"Data that should refresh hourly takes 2 days to load"
Full table reloads instead of incremental loads. No partitioning. No parallel processing. Pipelines built for a fraction of today's data volume, with no path to scaling.
"We're migrating to the cloud but our pipelines need rebuilding"
Legacy on-prem SSIS or Informatica pipelines that can't be lifted and shifted. The cloud migration is stalled waiting for pipeline re-engineering that the internal team doesn't have capacity to do.
What We Deliver

Six Types of Data Pipeline We Build and Maintain

From simple batch loads to complex real-time streaming - every pipeline we deliver is production-grade, documented, and monitored from day one.

Batch ETL Pipelines

Scheduled, reliable batch ETL pipelines that extract data from source systems — ERP, CRM, databases, flat files - transform it to your target schema, and load it into your data warehouse or lakehouse on a defined cadence. Full incremental load logic, error handling, and retry patterns.

Azure Data Factory Databricks Airflow
ELT Transformation Layer (dbt)

ELT pipelines using dbt to build and maintain your transformation layer on top of raw data in Snowflake, Databricks, or Fabric. Modular, version-controlled dbt models with automated testing, documentation, and a lineage graph your team can trust and maintain.

dbt Core dbt Cloud Snowflake
API & SaaS Data Ingestion

Automated data ingestion pipelines from Salesforce, HubSpot, Xero, Stripe, Google Ads, and 100+ SaaS platforms - using Fivetran connectors or custom API pipelines built in Python and ADF. Reliable, schema-aware, and handling API rate limits and pagination correctly.

Fivetran Python REST APIs
Incremental & CDC Pipelines

High-performance incremental load patterns and Change Data Capture pipelines that process only changed records - dramatically reducing load times and compute costs vs full table reloads. Watermark-based, timestamp-based, and log-based CDC implementations.

Debezium Delta Lake ADF
Pipeline Orchestration

End-to-end pipeline orchestration using Apache Airflow or Azure Data Factory - scheduling, dependency management, parallel execution, and failure handling across complex multi-step workflows. Replacing fragile cron jobs and manual triggers with a managed, observable orchestration layer.

Apache Airflow ADF Pipelines Fabric Pipelines
Pipeline Monitoring & Observability

Data pipeline monitoring and alerting built into every delivery - run status tracking, data freshness checks, row count anomaly detection, and Slack or email alerts when something fails or deviates. Existing pipelines can be retrofitted with our observability layer independently.

dbt Tests Great Expectations Azure Monitor
How We Deliver It

From Source Analysis to Live Pipeline in 4 Phases

First pipeline live in 2 weeks. Every sprint delivers tested, documented, production-ready pipeline increments, not a big bang at the end.

Source Analysis & Architecture

We audit your source systems, data volumes, update frequencies, and existing pipeline infrastructure. Then design the target architecture, ingestion pattern, transformation approach, orchestration tool, and monitoring strategy - before writing a single line of code.

⏱ Week 1
Sprint-Based Development

Pipeline development in weekly sprints - each delivering a working, tested increment. First pipeline is live in production within 2 weeks. Every sprint includes unit tests, integration tests, and documentation written alongside the code.

⏱ Weeks 2 onwards
Testing & Quality Validation

Automated data quality tests at every pipeline stage - row count validation, null checks, referential integrity, and business rule assertions. Every pipeline is tested against production-volume data before cutover. Zero tolerance for silent failures.

⏱ Ongoing each sprint
Deployment, Monitoring & Handover

Production deployment with monitoring dashboards, failure alerting, and runbook documentation. Full knowledge transfer to your team - they can maintain, extend, and troubleshoot every pipeline we've built without our involvement.

⏱ Final sprint
Why Numlytics

Why Choose Numlytics for ETL Pipeline Development

We've built production pipelines for enterprises across financial services, manufacturing, SaaS, and retail - in the US, UK, and Australia. Here's what makes our delivery different.

Production-Ready From Sprint 1
Every pipeline we build includes monitoring, error handling, retry logic, and documentation from the first sprint. We don't build PoCs that get promoted to production, we build for production from day one.
Certified Data Engineers Only
Every engineer is certified on the platforms they deliver - DP-203 Azure Data Engineer, Databricks Certified, dbt Developer. No generalists, no juniors learning your architecture on your project and your budget.
First Pipeline Live in 2 Weeks
We don't spend 6 weeks in discovery before writing a line of code. Our source analysis and architecture phase runs in week 1 - the first pipeline is in production by the end of week 2.
Zero Pipeline Failures at Cutover
We test every pipeline against production-volume data before cutover. Our data quality validation framework catches issues in testing, not after your business users notice stale dashboards on a Monday morning.
Documentation Written as We Go
Every pipeline is documented — architecture diagrams, data flow documentation, runbooks, and dbt model descriptions, written alongside the code, not as a final deliverable your team waits months for.
Up to 50% Lower Cost
Certified offshore data engineers from India - same technical depth as US or UK data engineering firms at up to 50% lower cost. Full timezone overlap, daily standups, and Slack access throughout.
★★★★★

"We had 60+ SQL scripts running on a shared drive that nobody fully understood. Every Monday there was a different data issue and we could never trace where it came from. Numlytics audited the entire estate, rebuilt it in Azure Data Factory and dbt in eight weeks, and added monitoring that alerts us within minutes of any failure. We haven't had a data incident since cutover - and that was seven months ago. The team can now extend and maintain everything themselves without our involvement."

DK
David K.
Head of Data Engineering · Manufacturing Group, United States

ETL pipelines are the foundation. These services build on top of what the pipelines deliver.

Data Warehouse Consulting
The target your ETL pipelines load data into
→
Data Lakehouse Architecture
Modern lakehouse as an alternative pipeline target
→
Data Quality Management
Quality validation built into every pipeline layer
→
Real-Time Data Streaming
When batch ETL isn't fast enough for your use case
→
All Data Engineering Services ↗
Full data engineering service hub
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FAQ

ETL Pipeline Development FAQs

Common questions before starting an ETL pipeline development engagement with Numlytics.

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ETL pipeline development is the process of building automated data pipelines that Extract data from source systems, Transform it into the required structure, and Load it into a target - typically a data warehouse or lakehouse. Modern ELT pipelines invert the order, loading raw data first and transforming it in the target system using tools like dbt. We build both patterns based on your use case.
For most modern cloud stacks, we recommend ELT - loading raw data into your warehouse or lakehouse first, then transforming using dbt. ELT is more scalable, easier to debug, and preserves raw data for reprocessing. Traditional ETL is still appropriate for high-volume pre-ingestion transformations, legacy system integrations, or data masking requirements. We recommend the right pattern per use case.
Numlytics delivers the first production pipeline within 2 weeks of kickoff. Complex programmes with multiple source systems, transformation layers, and orchestration typically run across 4–12 week sprint engagements - with a new tested, documented pipeline delivered each sprint. You see working output every week, not a big reveal at the end.
Yes, pipeline modernisation is one of our most common engagements. We migrate legacy SSIS, Informatica, or SQL Agent jobs to Azure Data Factory, Databricks, or dbt on a modern cloud platform. We audit your existing pipelines, identify what to refactor vs rebuild, and deliver the migration in sprints with zero-downtime cutover. See our data engineering services for the full scope.
Our primary stack includes Azure Data Factory for orchestration and ingestion, Databricks and Apache Spark for large-scale transformations, dbt for the ELT transformation layer, Apache Airflow for complex orchestration, Fivetran for SaaS connectors, and Snowflake or Microsoft Fabric as the target platform. We select tools based on your existing stack, not a preferred vendor list.
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Pipelines That Work in Production - From Week Two

Get production-grade ETL and ELT pipelines with monitoring, documentation, and zero failures at cutover. Certified data engineers. First pipeline live in 2 weeks. Proposal delivered within 24 hours. Serving enterprises in US, UK, Australia & UAE.

Get Free Consultation ← All Data Engineering Services
Other Data Engineering Services
Data Warehouse Consulting Data Lakehouse Architecture Real-Time Data Streaming Data Quality Management Business Intelligence