Meet Muoro's Leadership at Event: Snowflake World Tour, New York City•Date: October 15, 2026•Venue: Javits North, 445 11th Ave, New York|Meet Muoro's Leadership at Event: Consumer Goods Sales & Marketing Tech Summit, Chicago•Date: October 20–21, 2026•Venue: The Drake Chicago, 140 East Walton Place, Chicago|Meet Muoro's Leadership at Event: Databricks Data + AI World Tour, Atlanta•Date: November 10, 2026•Venue: Atlanta Marriott Marquis, Atlanta|Meet Muoro's Leadership at Event: Databricks Data + AI World Tour, New York City•Date: November 19, 2026•Venue: Javits Center, New York |
Meet Muoro's Leadership at Event: Snowflake World Tour, New York City•Date: October 15, 2026•Venue: Javits North, 445 11th Ave, New York|Meet Muoro's Leadership at Event: Consumer Goods Sales & Marketing Tech Summit, Chicago•Date: October 20–21, 2026•Venue: The Drake Chicago, 140 East Walton Place, Chicago|Meet Muoro's Leadership at Event: Databricks Data + AI World Tour, Atlanta•Date: November 10, 2026•Venue: Atlanta Marriott Marquis, Atlanta|Meet Muoro's Leadership at Event: Databricks Data + AI World Tour, New York City•Date: November 19, 2026•Venue: Javits Center, New York |
Meet Muoro's Leadership at Event: Snowflake World Tour, New York City•Date: October 15, 2026•Venue: Javits North, 445 11th Ave, New York|Meet Muoro's Leadership at Event: Consumer Goods Sales & Marketing Tech Summit, Chicago•Date: October 20–21, 2026•Venue: The Drake Chicago, 140 East Walton Place, Chicago|Meet Muoro's Leadership at Event: Databricks Data + AI World Tour, Atlanta•Date: November 10, 2026•Venue: Atlanta Marriott Marquis, Atlanta|Meet Muoro's Leadership at Event: Databricks Data + AI World Tour, New York City•Date: November 19, 2026•Venue: Javits Center, New York |
ETL Experts Who Build Pipelines You Can Trust
Nightly jobs that fail, reports that don’t match, and legacy ETL tools nobody wants to touch slow every decision downstream. Muoro’s ETL experts design, migrate, and run data pipelines that deliver accurate data on time, on modern platforms like dbt, Databricks, and Snowflake. For one global retail brand, our engineers cut pipeline failures from 15 a month to fewer than 2. Hire a single ETL expert or a full engineering pod, productive within 72 hours.
What our ETL experts deliver
Good ETL is invisible. The data arrives on time, the numbers agree, and nobody spends Monday morning fixing broken jobs.
What our ETL experts deliver
Good ETL is invisible. The data arrives on time, the numbers agree, and nobody spends Monday morning fixing broken jobs.
01
Pipelines that don’t break
Monitoring, retries, and data quality checks are built into every pipeline, so failures are caught and fixed before anyone sees a wrong number.
02
Faster reporting
Optimized transformations and incremental loads turn overnight batch runs into reports that are ready when your team needs them.
03
Numbers that reconcile
Every source is mapped, tested, and reconciled, so finance, operations, and leadership work from the same figures.
04
A clean exit from legacy tools
We move Informatica, SSIS and Talend workloads to modern platforms in controlled waves, with reporting live throughout.
ETL problems our experts fix
Our experts fix failing pipelines, inconsistent reports, and outdated ETL systems. We help you build reliable, faster data workflows.
ETL problems our experts fix
Our experts fix failing pipelines, inconsistent reports, and outdated ETL systems. We help you build reliable, faster data workflows.
Reports that don’t match
Sales says one number, finance says another. We trace every metric back to its source, fix the logic in one place, and reconcile the outputs.Legacy tools at end of life
Rising license costs, vendor support ending, and a shrinking pool of people who know the tool. We plan and run the move to a modern stack without a risky big-bang cutover.Batch jobs too slow for the business
Data that lands a day late is no use for pricing, inventory, or fraud decisions. We add incremental loads and streaming where it matters, and keep batch where it doesn’t.Failing nightly jobs
Pipelines break on schema changes, late files, or bad records, and someone has to rerun them by hand. We add data contracts, validation, and automated recovery so one bad file doesn’t stop the whole load.Reports that don’t match
Sales says one number, finance says another. We trace every metric back to its source, fix the logic in one place, and reconcile the outputs.Legacy tools at end of life
Rising license costs, vendor support ending, and a shrinking pool of people who know the tool. We plan and run the move to a modern stack without a risky big-bang cutover.Batch jobs too slow for the business
Data that lands a day late is no use for pricing, inventory, or fraud decisions. We add incremental loads and streaming where it matters, and keep batch where it doesn’t.Our ETL expertise
Our ETL experts cover the full pipeline, from source systems to the tables your analysts and AI models use.
Our ETL expertise
Our ETL experts cover the full pipeline, from source systems to the tables your analysts and AI models use.
01
Pipeline design and development
Batch and incremental pipelines from ERP, CRM, e-commerce, POS, APIs, and files, built in SQL and Python with dbt, Airflow, Azure Data Factory, or AWS Glue.
02
Data quality and reconciliation
Tests on every model, row-count and value checks between source and target, and alerts when data looks wrong, before it reaches a dashboard.
03
Orchestration and reliability
Scheduling, dependencies, retries, and monitoring, so pipelines run in the right order and recover on their own from common failures.
04
Real-time and streaming data
Change data capture and streaming with Kafka and Spark Structured Streaming, including upserts, deduplication, and exactly-once processing.
05
Governance and lineage
Access controls, data lineage, and a governed catalog, such as Databricks Unity Catalog, so every number can be traced back to where it came from.
ETL vs ELT: choosing the right approach
ETL transforms data before loading it into the warehouse. ELT loads raw data first and transforms it inside the warehouse. Most modern stacks use both, and the right mix depends on your data, not on fashion.
ETL vs ELT: choosing the right approach
ETL transforms data before loading it into the warehouse. ELT loads raw data first and transforms it inside the warehouse. Most modern stacks use both, and the right mix depends on your data, not on fashion.
ETL | ELT | |
|---|---|---|
Where transformation happens | In a separate tool or engine, before loading | Inside the warehouse or lakehouse, after loading |
Typical tools | Informatica, SSIS, Talend, AWS Glue, Spark | dbt, Fivetran, Snowflake, Databricks SQL |
Strong fit when | Sensitive data must be masked before it lands, or sources need heavy cleanup | You run a cloud warehouse with cheap, scalable compute |
Watch out for | Rigid workflows and slower change cycles | Uncontrolled raw data and rising compute costs |
How we migrate legacy ETL without putting your numbers at risk
A big-bang cutover is the fastest way to lose trust in your data. We move legacy ETL in waves, and prove each wave figure for figure before switching over.
How we migrate legacy ETL without putting your numbers at risk
A big-bang cutover is the fastest way to lose trust in your data. We move legacy ETL in waves, and prove each wave figure for figure before switching over.
Stage 1
Step 1: Inventory
We catalog every job, mapping, stored procedure, and report that depends on the legacy platform, and rank them by business risk.
Stage 2
Step 2: Plan the waves
Workloads are grouped into waves that can move independently, starting with the lowest-risk pipelines to prove the approach.
Stage 3
Step 3: Rebuild and run in parallel
Each wave is rebuilt on the target platform and runs alongside the old one, so reporting never goes dark.
Stage 4
Step 4: Reconcile figure for figure
Outputs from old and new pipelines are compared row by row and total by total. Any difference is explained and fixed before the wave is approved.
Stage 5
Step 5: Cut over and hand over
Once a wave reconciles, it goes live, and the legacy job is retired. Your team gets full documentation and runbooks for every pipeline.
Stage 1
Step 1: Inventory
We catalog every job, mapping, stored procedure, and report that depends on the legacy platform, and rank them by business risk.
Stage 2
Step 2: Plan the waves
Workloads are grouped into waves that can move independently, starting with the lowest-risk pipelines to prove the approach.
Stage 3
Step 3: Rebuild and run in parallel
Each wave is rebuilt on the target platform and runs alongside the old one, so reporting never goes dark.
Stage 4
Step 4: Reconcile figure for figure
Outputs from old and new pipelines are compared row by row and total by total. Any difference is explained and fixed before the wave is approved.
Stage 5
Step 5: Cut over and hand over
Once a wave reconciles, it goes live, and the legacy job is retired. Your team gets full documentation and runbooks for every pipeline.
ETL results from real client work
Leaving a legacy reporting platform without a single unexplained difference
Challenge
A reporting platform was past 85% capacity, vendor support was ending on a fixed date, and 15,000 pieces of legacy logic sat behind live reporting. Muoro migrated the estate in fixed-price waves, reconciling each one figure for figure before switching over, with reporting live throughout.
Result
100% of the estate moved and reconciled · zero unexplained differences at cutover · 6 of 6 milestones delivered in about 52 weeks, ahead of the vendor deadline.
ETL tools and platforms our experts work with
Our engineers know the legacy tools you run today and the modern platforms you are moving to, so we can keep the old system stable while building its replacement.
ETL tools and platforms our experts work with
Our engineers know the legacy tools you run today and the modern platforms you are moving to, so we can keep the old system stable while building its replacement.
Category | Tools |
|---|---|
Legacy ETL | Informatica PowerCenter, Microsoft SSIS, Talend |
Cloud ETL and ingestion | Fivetran, Azure Data Factory, AWS Glue |
Transformation | dbt, SQL, Python, Apache Spark |
Orchestration | Apache Airflow, Databricks Workflows |
Streaming | Apache Kafka, Spark Structured Streaming |
Warehouses and lakehouses | Databricks (Delta Lake, Unity Catalog), Snowflake, PostgreSQL |
Cloud | AWS, Azure, Google Cloud |
Ways to hire our ETL experts
Start with one engineer or a full team. Every option comes with engineers who own the outcome, not just the hours.
Ways to hire our ETL experts
Start with one engineer or a full team. Every option comes with engineers who own the outcome, not just the hours.
01
ETL engineering pod
A cross-functional team of ETL, data, and BI engineers that owns a defined outcome, such as a migration, a new data platform, or a reliability fix. Best for programs with a clear goal and deadline.
02
Build, operate, transfer
We build and run your ETL team for 3 to 6 months, put the processes and documentation in place, then transfer the team and full ownership to you. Best for companies building an in-house data function.
03
On-demand ETL experts
Add individual ETL engineers to your team to clear a backlog, cover a skills gap, or support a migration, without a long-term commitment.
Why companies choose Muoro for ETL
Why companies choose Muoro for ETL
01
Engineers who own delivery
Our engineers work inside your environment and stay accountable until the pipeline runs in production, not until the contract hours run out.
02
Proven in regulated, data-heavy environments
10+ years of production delivery, 125+ enterprise and growth-stage programs, and data platforms trusted by alternative-asset managers, lenders, and banks.
03
Clients who stay
More than 90% client retention across active programs.
04
Fast to start
Engineers are productive within 72 hours, and an engineer, not a salesperson, replies to every inquiry within four hours.
PARTNER + CERTIFICATE
Recognized by Platform Leaders. Trusted in Production.
PARTNER + CERTIFICATE
Recognized by Platform Leaders. Trusted in Production.
Frequently Asked Questions
An ETL expert designs, builds, and maintains the pipelines that move data from source systems, such as ERP, CRM, e-commerce, and payment platforms, into a warehouse or lakehouse. That covers extracting the data, cleaning and transforming it, loading it reliably, and monitoring it so reports and AI models always work from accurate, up-to-date data.
ETL transforms data before loading it into the warehouse. ELT loads raw data first and transforms it inside the warehouse, usually with a tool like dbt. ELT suits modern cloud platforms such as Snowflake and Databricks, while ETL still makes sense when sensitive data must be masked first, or sources need heavy cleanup. Most modern stacks use a mix of both.
We migrate in waves. Each group of pipelines is rebuilt on the new platform and runs in parallel with the old one while we reconcile the outputs figure for figure. A wave only goes live once every difference is explained, so reporting stays available throughout and the legacy jobs are retired one wave at a time.
It depends on how many pipelines you have and how much legacy logic they hold. A focused modernization can take about 90 days, as with our fashion ecommerce lakehouse. A large estate with thousands of legacy jobs runs in waves over roughly a year. After the initial inventory, we give you a wave plan with dates.
There is no single best tool. For most companies on a cloud platform, we recommend Fivetran or native connectors for ingestion, dbt for transformation, and Airflow or Databricks Workflows for orchestration. If you have significant investment in Informatica or SSIS, we help you decide what to keep, what to move, and in what order.
Every pipeline includes automated tests for freshness, completeness, and valid values, plus row-count and total checks between source and target. Alerts fire when something looks wrong, before the data reaches a dashboard. During migrations, we reconcile old and new outputs figure for figure before anything goes live.
Yes. Our engineers work inside your environment, using your repositories, cloud accounts, and ways of working. They can join your existing data team, lead a defined project as a pod, or build a new team that we later transfer to you.
Our engineers are typically productive within 72 hours of kickoff. To get started, share what you need; an engineer, not a salesperson, replies within four hours to discuss your pipelines and the right engagement model.
Cost depends on the engagement model and scope. Well-defined migrations can be delivered at a fixed price, pods are priced per team for a defined outcome, and on-demand experts are billed for the time you need. After a short call about your pipelines and goals, we give you a clear estimate.
Build reliable ETL pipelines with Muoro
Tell us about your data sources, current pipeline challenges, and migration goals. We'll help you choose the right ETL experts and engagement model.
Talk to a certified expert