A Managed Data Team Is Not Just a Consultant
Your managed data engineering squad is a permanent, dedicated extension of your organisation — staffed with specialists, governed end-to-end, and aligned entirely to your data roadmap. Not a one-time project. Not a black-box vendor.
Dedicated Data Squad
Data Engineers, Analytics Engineers, BI Developers, and ML Ops specialists — 100% allocated to your pipelines, models, and dashboards. No shared bench, no rotating contractors.
Structured Delivery Governance
Sprint cadences, SLA dashboards, data quality reporting, and an escalation matrix built in from Day 1. Full operational transparency across every layer of your stack.
Data Security & IP First
NDA and IP ownership agreements, GDPR and SOC-2 aligned processes, encrypted environments, role-based access controls, and audit-ready infrastructure by default.
High cost. Zero continuity.
- ✕Project-based engagement ends when the statement of work does
- ✕No knowledge retention — every handover loses critical context
- ✕Hidden agency markups of 40–60% on every consultant billed
- ✕No accountability for data quality, pipeline uptime, or SLAs
- ✕IP and data model ownership ambiguity by default
Dedicated. Managed. Accountable.
- ✓Dedicated ongoing team — 100% allocated to your data roadmap
- ✓Long-term knowledge retention and institutional data context
- ✓Transparent cost-plus model — no hidden margins, ever
- ✓SLA ownership, data quality KPIs, and pipeline uptime accountability
- ✓Watertight NDA and full IP assignment from Day 1
Every Data Role. Fully Managed.
From a single Senior Data Engineer to a full cross-functional analytics squad of 15+, we recruit, onboard, and operate the right mix of skills for your data maturity stage.
Data Engineers
Design, build, and maintain scalable data pipelines — from source ingestion to clean, modelled, analytics-ready datasets. Experts in batch and streaming architectures.
Analytics Engineers
Transform raw pipeline outputs into clean, trusted data models using dbt. Bridge the gap between data engineering and business intelligence with rigorous testing and documentation.
BI Developers
Build self-service dashboards, executive reporting layers, and embedded analytics experiences that turn your data models into real business decisions.
ML Ops Engineers
Deploy, monitor, retrain, and govern machine learning models in production. Build the infrastructure that keeps AI reliable, observable, and continuously improving.
Cloud Data Architects
Design scalable, cost-optimised cloud data platform architectures — lakehouses, warehouses, streaming meshes — across AWS, GCP, and Azure.
Data Quality & Governance Engineers
Implement data quality frameworks, observability tooling, cataloguing, lineage tracking, and governance policies that make your data trustworthy at scale.
End-to-End Data Engineering Services
From pipeline architecture and warehouse design to ML Ops and real-time analytics — your managed team delivers across the full data value chain.
Batch · Streaming · ELT / ETL
Design and build robust, observable, and scalable data pipelines — from CDC ingestion and API connectors to multi-hop transformation layers. Airflow, Spark, Kafka, and dbt at the core.
Snowflake · BigQuery · Databricks · Redshift
Architect, optimise, and manage modern cloud data platforms. Medallion architecture, cost governance, query performance tuning, and warehouse-to-lakehouse migration strategies.
Trusted data models for self-service analytics
Build, test, document, and deploy layered dbt models (staging → intermediate → marts). Establish a semantic layer your analysts and BI tools can trust without engineering involvement.
Dashboards · Self-service · Embedded analytics
Develop executive and operational dashboards that surface the right metrics at the right time. Self-service BI capability enabling non-technical stakeholders to answer their own questions.
Production ML infrastructure end-to-end
Deploy machine learning models to production, automate retraining pipelines, monitor drift, and maintain feature stores. The infrastructure layer that makes AI sustainable, not just experimental.
Quality · Lineage · Cataloguing · Trust
Implement data quality frameworks, monitoring alerting, catalogue and lineage tooling, PII detection, and governance policies. Turn data from a liability into a reliable, audited asset.
What Our Clients Report After 12 Months
Full Modern Data Stack. Any Architecture.
Our pre-vetted data engineers cover the complete modern data toolchain — from ingestion and orchestration to serving and ML operations.
Orchestration
Ingestion & Integration
Storage & Warehousing
Transformation
Business Intelligence
ML Ops & AI Infrastructure
Cloud Platforms
Observability & Quality
Cataloguing & Governance
Work the Way You Need To.
Three flexible models built for different data maturity stages — from scaling an existing analytics function to building a full offshore data capability centre.
Monthly Managed Data Team
Dedicated data engineers and analysts on an ongoing monthly engagement. Your team, your stack, your sprint cadences — fully managed and continuously optimised by us.
Best for
- ✓Long-term data platform development
- ✓Ongoing pipeline maintenance & iteration
- ✓Continuous BI & reporting delivery
Build-Operate-Scale
We build your data team from scratch, operate all delivery processes and infrastructure, and scale your data capability over time — the practical alternative to building in-house.
Perfect for
- ✓Companies with no existing data function
- ✓Building long-term offshore data capability
- ✓Multi-domain and enterprise data programs
Flexible Data Team Scaling
Scale your managed data team up or down based on project phases, sprints, or business demand — without hiring overhead or agency renegotiations on your side.
Ideal for
- ✓Fast-growing startups with variable data sprints
- ✓Seasonal analytics or reporting peaks
- ✓Data migration or warehouse modernisation projects
Your Data Team. Live in 2–4 Weeks.
From signed agreement to your first pipeline running in production — every step owned by us, zero surprises on your end.
Day 1–3
🔍 Data Discovery & Roadmap Alignment
We audit your current data estate, understand your analytics goals, pipeline pain points, BI requirements, and ML ambitions. We define the right team composition, toolchain, and delivery cadence — no generic solutions.
Week 1–2
🎯 Team Formation & Talent Acquisition
We recruit Data Engineers, Analytics Engineers, BI Developers, and ML Ops specialists aligned to your stack and domain. Every candidate goes through rigorous technical screening. You make all final hiring decisions — always.
Week 2–3
🔐 Infrastructure & Access Setup
Secure cloud data environment configuration, VPN access, data warehouse credentials, repository onboarding, and IP / NDA agreements implemented. Your data assets remain fully protected from Day 1.
Week 3–4
🚀 Onboarding & First Sprint
Your data team integrates into Slack, Jira, GitHub, and your agile delivery processes. Sprint backlog aligned, data models scoped, and first pipeline in development by end of Week 4. Zero friction onboarding.
Continuous
⚡ Ongoing Management & Optimisation
We continuously manage team performance, pipeline SLAs, data quality KPIs, retention, and scalability. Regular delivery reviews, performance dashboards, and proactive escalation handling — all owned by us so you can focus on business outcomes.
Why Choose Us as Your Data Team Partner
We don’t sell data consultants on day rates. We build and operate the data engineering arm of your business — with full accountability at every layer.
Faster Pipeline Delivery
Traditional data hiring takes months. We onboard specialist data engineering teams within 2–4 weeks using a streamlined recruitment and delivery process, so your data starts flowing faster.
Structured & Transparent Model
Clearly defined SLAs, reporting frameworks, data quality KPIs, and measurable delivery metrics. Full visibility into pipeline health, team productivity, and costs — no black-box complexity.
Access to Top Indian Data Talent
Pre-vetted data engineers, analytics engineers, and ML Ops specialists sourced through a rigorous hiring process and committed to long-term collaboration with your team.
Data Security & IP Protection
NDA agreements, encrypted data environments, role-based access controls, and compliance-driven processes. Your data assets and intellectual property remain fully protected — always.
Cost-Optimised Data Scaling
Reduce operational costs by 60–70%, access a wider specialist talent pool, and scale your data function without infrastructure investment or hidden agency markups on every engineer billed.
Transparent Reporting
Regular pipeline health reports, team performance dashboards, data quality scorecards, and open communication channels. Full visibility at every stage, no surprises on your end.
Built for Every Data Maturity Stage.
Whether you’re a startup instrumenting your first data warehouse or an enterprise consolidating a multi-cloud data mesh, our managed data teams scale with you.
Startups
Stand up a production-grade data stack without hiring a full in-house data function. Get a senior-led data engineering team operational in weeks — data warehouse, pipelines, and dashboards included.
SMBs
Scale your analytics capability cost-effectively. Access specialist data engineering talent at a fraction of the in-house cost, with full operational management and transparent SLA reporting.
Enterprises
Build dedicated offshore data engineering capability centres with governance, security, and enterprise-grade tooling. No entity setup, no $100k+ upfront investment. Operational under 30 days.
Data Teams Built for Your Sector.
Our managed data engineers understand industry-specific data models, compliance requirements, and the metrics that drive decisions in your domain.
Risk analytics, fraud detection pipelines, regulatory reporting (Basel, AML), real-time transaction monitoring, and customer 360 data platforms with strong data lineage and audit trails.
HIPAA-compliant data pipelines, patient outcome analytics, EHR data integration, clinical trial reporting, and population health analytics on secure cloud infrastructure.
Product analytics pipelines, user behaviour modelling, churn prediction infrastructure, revenue attribution, and self-serve BI capabilities for fast-moving product organisations.
Demand forecasting pipelines, inventory optimisation models, customer segmentation, marketing attribution, and real-time personalisation data infrastructure.
Data Engineering Teams — Your Questions Answered
What is a Managed Data Engineering Team? +
A Managed Data Engineering Team is a dedicated, fully staffed squad of data specialists — Data Engineers, Analytics Engineers, BI Developers, and ML Ops engineers — recruited, managed, and operated by ManagedTeams.co and allocated 100% to your data roadmap. Unlike consultants, they are long-term, accountable team members who build deep institutional knowledge of your data estate.
How quickly can a data engineering team be operational? +
A structured data engineering team can typically be onboarded and producing work within 2–4 weeks from signed agreement. Our phased recruitment and onboarding process ensures fast initial deployment without compromising candidate quality or security setup.
Who owns the data models, pipelines, and code built by the team? +
100% of all intellectual property, code, data models, dbt projects, dashboards, and pipeline infrastructure belong to you. Watertight NDA and IP ownership agreements are implemented from Day 1 — your data assets are always yours, with zero ambiguity.
Do I need to set up a legal entity in India? +
No. ManagedTeams.co handles all HR, payroll, compliance, and operational infrastructure on your behalf. You get all the benefits of a dedicated data engineering team in India without needing to register a company or invest in Indian infrastructure.
What data tools and technologies does the team specialise in? +
Our pre-vetted engineers cover the full modern data stack: orchestration (Airflow, Prefect, Dagster), ingestion (Fivetran, Airbyte, Kafka), warehousing (Snowflake, BigQuery, Databricks, Redshift), transformation (dbt), BI (Tableau, Power BI, Looker), ML Ops (MLflow, SageMaker, Vertex AI), and observability (Monte Carlo, Great Expectations). We match team composition to your specific toolchain.
Can I scale the data team as our data needs grow? +
Yes. Scalability is a core design principle of our engagement model. You can expand team size, introduce new specialist roles (e.g. add an ML Engineer once pipelines are stable), or reduce capacity based on project phases — all with zero hiring overhead on your side.