Dedicated Data Engineering Team - Built Around Your Pipeline, Your Stack
A dedicated data engineering team from Numlytics gives you a permanent offshore pod - certified Azure, Fabric, and Databricks engineers - integrated directly into your delivery workflow. No contractors, no shared pools. One team, fully yours, operational in 2 weeks at 55% of the onshore cost.
- Fully operational in 2 weeks -not 3 months
- Microsoft, Databricks & AWS certified engineers
- Works inside your sprint, repo & DevOps tooling
- 55% lower cost than equivalent onshore team
scoping to first sprint
an onshore DE team
across cloud & modern stack
- never shared across clients
A Permanent Engineering Team That Operates Inside Your Delivery Workflow
A dedicated data engineering team from Numlytics is not a project squad - it is a permanent offshore engineering function assigned exclusively to your organisation. Your team builds pipelines, maintains warehouses, and ships data products inside your own sprint cadence, repositories, and cloud environment.
Unlike staff augmentation, where individuals slot into your team one at a time, a dedicated DE pod arrives pre-assembled - an engineering lead, one or more senior data engineers, and a DevOps-aware QA - with established working practices, code standards, and delivery rituals from Day 1.
Our offshore data engineering team model is designed for enterprises in the US, UK, and Australia that have a pipeline backlog they can't clear, a platform migration that needs engineering muscle, or a growing data estate that has outpaced their internal team's capacity. You get the engineering depth of a full internal team at a fraction of the cost.
Six Engineering Capabilities. One Dedicated Team.
Every capability is delivered by your named team - inside your tools, your sprint, your cloud environment.
Your team builds, maintains, and monitors the full data pipeline layer - batch and streaming ingestion, transformation logic, and orchestration - using your existing Azure, Databricks, or Airflow environment. No pipeline debt left unresolved.
- Batch & streaming ingestion pipelines
- dbt transformation layer & testing
- Airflow / ADF orchestration & scheduling
Whether you're migrating to Microsoft Fabric, implementing a Databricks Lakehouse, or consolidating legacy warehouses into Snowflake, your dedicated team leads the engineering work end-to-end - with no disruption to your existing BI layer.
- Microsoft Fabric & Lakehouse migrations
- Snowflake & Databricks platform builds
- Legacy warehouse consolidation & cutover
Your team designs and maintains the semantic and physical data models that underpin your BI and analytics estate - star schemas, medallion architecture, normalisation, and dimensional design - keeping your warehouse clean as it grows.
- Star schema & dimensional modelling
- Medallion layer (Bronze / Silver / Gold)
- Semantic model governance & versioning
Your team implements a data quality framework - automated testing, freshness checks, anomaly detection, and lineage tracking - so data issues are caught in the pipeline before they reach your dashboards or decision-makers.
- dbt tests & Great Expectations integration
- Data freshness & schema drift alerting
- End-to-end lineage documentation
Your team implements DataOps practices - version-controlled pipelines, automated testing, CI/CD for dbt and ADF, and environment promotion - so your data platform evolves at the pace of your product, not your deployment queue.
- Git-based pipeline version control
- CI/CD for dbt, ADF & Fabric Notebooks
- Dev / staging / prod environment management
For organisations that need real-time data, your team builds event-driven pipelines using Kafka, Azure Event Hubs, or Spark Structured Streaming - bringing live operational data into your analytics and ML layer without batch latency.
- Kafka & Event Hubs stream ingestion
- Spark Structured Streaming pipelines
- Real-time dashboards & alerting integration
From Scoping to First Sprint - in 4 Steps
Four structured steps from requirements to a fully operational dedicated engineering team.
We audit your existing data platform, pipeline backlog, cloud environment, and team structure - then define the exact roles, seniority levels, and team composition you need for your roadmap.
⏱ Day 1–3We match your requirements against our certified talent pool - presenting 2–3 candidates per role with technical assessment results, relevant stack experience, and sample work within 5–7 business days.
⏱ Day 4–10Your team is onboarded into your cloud environment, Git repositories, Azure DevOps or Jira, and communication channels. Code standards, branching strategy, and sprint rituals are agreed before the first commit.
⏱ Week 2Your dedicated team runs in your sprint cadence - planning, standups, reviews, and retrospectives. Monthly velocity reviews and quarterly scope alignment ensure the team evolves with your platform and priorities.
⏱ Ongoing
Six Reasons Enterprises Build Their DE Team With Numlytics
Certified engineers, your stack, your sprint - with none of the recruitment, attrition, or overhead.
"We were six months into a Fabric migration with a two-person internal team that was already running flat out on BAU. We engaged Numlytics for a dedicated data engineering team - three engineers and a lead. Within two weeks they were committing code in our DevOps repo, running in our sprints, and shipping the migration work our internal team couldn't reach. We hit our migration deadline. I've worked with offshore teams before that never really integrated - this was genuinely different. They knew our platform better than some of our own people after two months."
Looking for a different engagement model or a specific engineering specialism? Explore the full Numlytics offshore and engineering offering.
Common Questions About Dedicated Data Engineering Teams
Everything you need to know before engaging Numlytics to build your dedicated engineering pod.
Get Free Consultation →Certified engineers in your sprint by Week 2.
Tell us your stack, backlog, and team size - we'll match certified engineers and present profiles within 5 business days.