Hire Data Engineer — production pipelines, clean warehouse data
Most data projects fail on plumbing, not ambition: stale dashboards, silent schema changes, and warehouse bills nobody understands. A dedicated data engineer fixes the foundation — reliable Airflow and dbt pipelines, modeled marts on Snowflake or BigQuery, and data quality tests that catch breakage before your team notices.
Senior oversight on every delivery: I'm Omer Muneer Qazi, a Dubai-based Fractional CTO & Solutions Architect with 15+ years of experience and 100+ projects delivered across 6 countries. Deploying models too? Hire an MLOps engineer or contact me to start.
Data Engineering Deliverables, Built for Production
Airflow Ingestion Pipelines
Nightly and hourly DAGs that pull from your SaaS APIs and databases into the warehouse, with retries, alerting, and backfill support so a failed run never means lost data.
dbt Transformation Models
Versioned SQL models — staging, marts, and snapshots — with documented sources and CI checks, so business logic stays consistent, reviewable, and safe to change across the team.
Snowflake / BigQuery Warehouse Setup
Schemas, roles, and compute sizing configured from day one, with cost controls and clustering so queries stay fast while the monthly bill stays predictable as data volume grows.
CDC Ingestion
Change-data-capture streams from Postgres or MySQL that land fresh rows in your warehouse within minutes, without heavy queries hammering production or nightly batch windows ever again.
Data Quality Tests & Monitoring
dbt tests, freshness checks, and anomaly alerts on every critical table, so broken sources, null spikes, and schema drift get caught before dashboards lie to your stakeholders.
Analytics-Ready Data Marts
Fact and dimension tables modeled for your BI tool, with clear naming and documentation so analysts answer questions themselves instead of filing tickets with your data team.
From Audit to Handover in Weeks
A structured engagement with no surprises — you’ll always know what’s happening and what’s next.
Source & Pipeline Audit
I map your sources, schemas, and existing pipelines to find gaps in freshness, quality, and cost before writing a line of code.
Architecture & Data Modeling
Pipeline design, warehouse layout, and dbt model structure agreed up front, so the build has no surprises and no rework.
Iterative Build
Pipelines, models, and tests ship in weekly increments with real data flowing from the first week, not a big-bang reveal.
Docs & Handover
Runbooks, data dictionaries, and monitoring dashboards your team can actually run, plus a handover call to walk through everything end to end.
Why Hire Through Omer Muneer Qazi
I’ve shipped data platforms with teams at Phaedra Solutions, Integriti, Napollo, Nabidios, Nello, and EverestX, and delivered 100+ projects across 6 countries that enabled $25M+ in client revenue. You get practitioner judgment on pipeline design, warehouse cost control, and data quality — not just code.
I’m Dubai-based and work worldwide, so you get senior coverage across time zones. Pipelines are boring on purpose: no clever hacks, just reliable data your business can plan on.
Data Engineer FAQs
How long until our pipelines are in production?
A typical first pipeline — ingestion, dbt models, and tests — ships in two to three weeks. Full warehouse rebuilds with CDC and historical backfills usually take six to eight weeks, scoped after the audit.
Airflow or a managed orchestrator?
Airflow when you need full control and have the team to run it; managed options like Dagster Cloud or Fivetran when you want less ops overhead. I’ll recommend based on your team size, not my preferences.
How do you handle schema drift from source APIs?
Raw layers land data as-is with schema versioning, and dbt contracts plus tests flag unexpected columns or type changes before they break downstream models. You hear about drift from an alert, not a broken dashboard.
Snowflake or BigQuery — which should we pick?
It depends on your workload: Snowflake for flexible compute and multi-cloud, BigQuery for serverless scale and tight GCP integration. Either way, I set up cost controls so spend stays predictable as usage grows.
Will you work with our in-house team?
Yes — most engagements pair me with your engineers for knowledge transfer. I document everything, review their PRs on pipeline code, and hand over runbooks so your team owns the system confidently.
Hire a Data Engineer
Tell me about your sources, warehouse, and deadlines. You’ll get a scoped plan with timelines and a fixed quote — no vague estimates.