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Data Engineering

ETL Pipeline Development: Reliable Data Flows From Source to Insight

Your data platform is only as good as the pipelines feeding it. Fragile, undocumented ETL jobs that break at 2 AM and take days to debug — that's the status quo we replace with production-grade pipeline engineering built for scale, observability, and change.

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🔄

Modern ELT Architecture

dbt, Fabric Dataflows, Spark — transform inside the warehouse, not before it

🏭

Orchestration

Airflow, ADF, Fabric pipelines — DAG-based scheduling with dependency management

✅

Data Quality Gates

Automated validation, schema checks, anomaly detection at every pipeline stage

📊

Pipeline Observability

Monitoring, alerting, lineage, and SLA tracking for every data flow

20+
Technology domains with pipeline expertise
4.3
Day avg to first curated profile
92%
First-match acceptance rate
200+
Pre-qualified delivery partners

ETL is the plumbing of every data platform. When it breaks, everything downstream stops.

Every dashboard, every ML model, every analytics report depends on data arriving on time, in the right format, with the right quality. Yet ETL pipelines are often the most neglected infrastructure in the data stack — built quickly to solve an immediate need, then left to accumulate technical debt until they fail at the worst possible time.

Modern ETL/ELT development is software engineering applied to data movement. It means version-controlled transformations (dbt), DAG-based orchestration (Airflow, ADF), automated data quality gates (Great Expectations), incremental loading strategies that reduce compute costs, and observability that tells you what went wrong, where, and why — before stakeholders notice.

Xylity matches pipeline engineers who build data flows as production software — with testing, CI/CD, monitoring, and documentation. Whether you're migrating from SSIS to Fabric pipelines, building a medallion architecture on Databricks, or refactoring a tangled web of stored procedures into clean dbt models — our consulting-led matching ensures you get engineers with the right platform depth.

40%
of data engineering time is spent maintaining and debugging existing pipelines rather than building new ones — a direct result of undocumented, untested ETL code. Modern pipeline development eliminates this tax by treating data transformations as tested, version-controlled software.
See our full DE practice →
What we deliver

ETL/ELT pipeline development capabilities

Every pipeline engagement is staffed by engineers who treat data movement as production software — with testing, versioning, monitoring, and documentation built in from the start.

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dbt Transformation Development

Modular, tested, version-controlled SQL transformations. Staging models, intermediate transformations, mart definitions, tests, and documentation. The modern standard for analytics engineering that replaces stored procedure spaghetti.

🏭

Orchestration & Scheduling

DAG-based pipeline orchestration using Airflow, Azure Data Factory, Fabric pipelines, or Prefect. Dependency management, retry logic, SLA monitoring, and failure alerting. Production-grade scheduling that handles complexity gracefully.

⚡

Incremental Loading & CDC

Move only what changed. Incremental loading strategies, merge patterns, and change data capture implementations that reduce compute costs by 80%+ compared to full-reload approaches while maintaining data freshness.

See data integration →
✅

Data Quality Automation

Automated quality gates using Great Expectations, dbt tests, or custom validation frameworks. Schema validation, null checks, referential integrity, business rule assertions, and anomaly detection — at every pipeline stage, not just the end.

🔀

Legacy Pipeline Migration

Migrating from SSIS, Informatica, Talend, or stored-procedure-based ETL to modern cloud-native tools. Not a one-to-one translation — a re-architecture that leverages your target platform's strengths.

See cloud migration →
📊

Pipeline Monitoring & Observability

Execution monitoring, data lineage, cost tracking, and SLA dashboards. Know what ran, what failed, what's slow, and how much it costs — before anyone asks. Monte Carlo, Elementary, or custom observability implementations.

Pipeline tools

Technologies we build with

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dbt

SQL transformations, testing, documentation, incremental models, packages

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Apache Airflow

DAG orchestration, sensors, operators, XComs, task groups, pools

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Azure Data Factory

Copy activities, data flows, linked services, integration runtimes, triggers

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Fabric Pipelines

Dataflows Gen2, notebooks, copy jobs, shortcuts, OneLake integration

⚡

Spark / PySpark

Large-scale transformations, Delta Lake writes, DataFrame operations

📊

Fivetran / Airbyte

Managed ELT, pre-built connectors, schema normalization, CDC sync

✅

Great Expectations

Data quality testing, validation suites, profiling, documentation

🔍

Monte Carlo / Elementary

Data observability, pipeline monitoring, anomaly detection, lineage

How we deliver

Pipeline engineers matched to your data stack

Pipeline Assessment

We map your data sources, target platform, existing pipelines, and pain points. The matching starts from your specific stack and scale requirements.

Engineer Matching

Pipeline developers matched for your tools (dbt, Airflow, ADF, Fabric) and platform (Snowflake, Databricks, Fabric). Production pipeline experience verified through scenario assessment.

Build & Test

Pipelines built as production software: version-controlled, tested, documented, and monitored. Incremental loading, quality gates, and observability from day one.

Optimize & Transfer

Performance tuning, cost optimization, monitoring setup, and knowledge transfer. Your pipeline infrastructure is production-grade and your team owns it.

Who we serve

Pipeline expertise for enterprises and IT services companies

For enterprises

Data platform invested but pipelines still fragile?

You've built the lakehouse. Now you need the engineering discipline to feed it reliably. Xylity matches pipeline engineers who build data flows as production software — with testing, CI/CD, incremental loading, and observability built in from the start. Our consulting-led approach starts from your platform architecture and data sources.

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For IT services companies

Client needs modern pipeline engineering your bench can't cover?

dbt, Airflow, medallion architectures, incremental loading — modern pipeline development requires specific tool expertise that generalist SQL developers don't have. When your client's project needs modern data engineering, Xylity delivers curated pipeline developer profiles from our 200+ partner network. First profiles in an average of 4.3 days.

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Common questions

ETL pipeline development — answered

What's the difference between ETL and ELT?
ETL transforms data before loading it into the target — traditional for on-prem warehouses with limited compute. ELT loads raw data first, then transforms inside the target platform — modern approach for cloud warehouses (Snowflake, Databricks, Fabric) that have abundant compute. Most modern architectures use ELT with dbt for transformations.
Should we use dbt for our transformations?
dbt is the modern standard for analytics engineering transformations. If your target platform supports SQL (Snowflake, Databricks SQL, BigQuery, Fabric), dbt gives you version control, testing, documentation, and modularity. For complex Spark-based transformations, PySpark notebooks may be more appropriate. Many architectures use both.
How do you handle pipeline failures?
Production pipelines need automated retry logic, idempotent operations (safe to re-run), dead-letter queues for failed records, alerting with context (what failed, why, and what to do), and documented runbooks. We build all of this into every pipeline — failure handling isn't an afterthought, it's a design requirement.
Can you migrate our SSIS pipelines to cloud-native tools?
Yes. SSIS-to-cloud migration is one of our most common pipeline engagements. We map every SSIS package, identify dependencies, and re-architect for ADF, Fabric pipelines, or Airflow — leveraging cloud-native features (incremental loading, parallel execution, serverless compute) that SSIS doesn't support. See our cloud migration service.

Your data platform deserves pipelines
built as production software.

Tell us about your data sources, target platform, and pipeline requirements. We'll match engineers who build reliable, tested, observable data flows.

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