--- title: "Managed Data Services and Data Engineering | Azendo" description: "Managed data services from an assigned team in Thailand. Pipelines, warehouse modelling, data quality and monitoring under one monthly agreement." url: "https://azendo.co/services/data-engineering/" --- [← All services](https://azendo.co/services/) # Managed data services, run by people rather than a dashboard. Pipelines break on a Tuesday. Someone changes a column name upstream and a report quietly starts lying. Managed data services at Azendo means an assigned data engineer who notices, fixes it, and tells you what happened. Your warehouse, your cloud account, our people. [Contact us](https://azendo.co/services/data-engineering/#team-builder) [Talk to a service delivery manager](https://azendo.co/get-in-touch/) How the role joins your team Added to delivery \+ capacity Data Engineer Assigned capacity * [Python](https://azendo.co/skills/python/) * [Airflow](https://azendo.co/skills/airflow/) * [Snowflake](https://azendo.co/skills/snowflake/) Capacity 160 h committed monthly ## What are managed data services? Managed data services means an external provider running data pipelines, warehouse models, quality checks and monitoring continuously, rather than delivering a data platform and handing it over. The distinction matters because data failures are usually silent: a pipeline dropping rows produces a dashboard that is confidently wrong. ## What managed data services cover at Azendo. Skills and scope are agreed before delivery starts, so the first sprint is productive rather than a ramp-up month. The work runs to the same sources, the same repositories and the same data contracts as your own team’s. Managed data services here means assigned engineers doing the work, not managed infrastructure. Your warehouse, your cloud account, your data. We operate inside it and nothing moves to us. Most engagements start with pipelines someone else built and left behind. The first work is usually establishing what actually runs, what it depends on, and which of the nightly jobs nobody has looked at in a year are still load-bearing. * Pipeline development Ingestion and transformation from the systems you already run. * Warehouse modelling Dimensional models an analyst can query without asking an engineer first. * Data quality and testing Tests on the data itself, so a broken load surfaces before a board meeting does. * Monitoring and on-call Freshness and volume alerting, with someone assigned to answer them. * Documentation and lineage So the answer to “where does this number come from” takes a minute. ## Assigned to fit the stack you already run. Warehouses: Snowflake, BigQuery, Redshift, Databricks, Postgres. Transformation and orchestration: dbt, Airflow, Dagster, Fivetran. Streaming: Kafka, Kinesis. Reporting layer: Looker, Power BI, Metabase, Tableau. The reporting layer is where data problems become visible, but it is rarely where they start. A dashboard that disagrees with finance is almost always a modelling or ingestion problem two layers down, and that is where an assigned data engineer spends most of their time. If your stack is not listed, ask. Legacy systems with no clean extraction path are a common part of this work rather than an exception to it. Pipelines and orchestration * [Airflow](https://azendo.co/skills/airflow/) * [dbt](https://azendo.co/skills/dbt/) * [Python](https://azendo.co/skills/python/) * [Fivetran](https://azendo.co/skills/fivetran/) * [Prefect](https://azendo.co/skills/prefect/) * \+ more on request Warehouses * [Snowflake](https://azendo.co/skills/snowflake/) * [BigQuery](https://azendo.co/skills/bigquery/) * [Redshift](https://azendo.co/skills/redshift/) * [Databricks](https://azendo.co/skills/databricks/) * [ClickHouse](https://azendo.co/skills/clickhouse/) * \+ more on request Streaming and infra * [Kafka](https://azendo.co/skills/kafka/) * [Spark](https://azendo.co/skills/spark/) * [Docker](https://azendo.co/skills/docker/) * [Flink](https://azendo.co/skills/flink/) * [RabbitMQ](https://azendo.co/skills/rabbitmq/) * \+ more on request ## One fixed fee for a monthly average. Working hours across a year, minus leave and public holidays, divided by twelve. The fee holds steady whether the month was quiet or a migration weekend. 160 h Typical monthly capacity for one data engineer Fixed Monthly price, unaffected by leave or holidays 4–6 weeks From signed scope to delivery starting Monthly Cycle to raise or lower committed hours Pipelines built by a team that learns your data model once and keeps it. Chiang Mai, Thailand — five hours ahead of Northern Europe ## The data engineering roles we assign. Take one role, or several as one delivery team. Each role below has its own page describing what it delivers under a service agreement. [Data Engineer Pipelines, models and data tests Read about the role →](https://azendo.co/services/data-engineering/data-engineer/)[Analytics Engineer The modelling layer your analysts use Read about the role →](https://azendo.co/services/data-engineering/analytics-engineer/)[ETL Developer Movement of data between systems Read about the role →](https://azendo.co/services/data-engineering/etl-developer/)[Database Administrator Performance, backups and access Read about the role →](https://azendo.co/services/data-engineering/database-administrator/) ## Building in-house, a local agency, or Azendo. Each fits a different situation. An in-house role makes sense when the work is permanent and local; a local agency suits a one-off project with a clear end date. Azendo sits between the two — ongoing capacity for work that keeps coming, with the team, the workplace and the administration behind it handled on our side. | Comparison | Building it in-house | Local agency | Azendo | | ------------------------------- | ---------------------------------- | ----------------------------------------- | ----------------------------------------- | | Time to productive output | Months — recruit, onboard, ramp up | Fast to start, slow to learn your product | 4–6 weeks | | Continuity of context | Resets when someone leaves | Rebuilt with each new project | Held by the same delivery team, for years | | Continuity of product knowledge | Lost when the hire leaves | Ends with the project | Held by the assigned team | | Who answers for delivery | You do | Account manager, between projects | A service delivery manager, continuously | | Cost profile | Fixed, whatever the workload | Priced per project | One monthly fee, adjustable each cycle | | Scaling a discipline | A new hire each time | Re-scoped each engagement | Capacity up or down at the monthly cycle | ## Questions about managed data services. Is this managed hosting for a database? No. Managed data services here means assigned data engineers doing the work, not managed infrastructure. Your cloud account stays yours and we operate inside it. What is the difference between this and data analytics outsourcing? Data analytics outsourcing usually means handing over analysis and receiving answers. This is the layer underneath, building and running the pipelines and models the analysis depends on. Several partners buy both. Can you take over an existing warehouse? That is the common starting point. Most engagements begin with something built by a team that has since moved on. Who owns the data and the code? You do, entirely. Work happens in your cloud account and your repositories. Can data engineering be combined with data science? Yes, under one agreement. They are separate service lines that often belong on the same team. ## Often assigned alongside a data engineer. A specialist rarely works alone on a roadmap. These disciplines cover the ground around the role and can be added to the same service agreement. [Data science Model integration, fine-tuning and evaluation](https://azendo.co/services/data-science/)[AI engineering LLM features, retrieval and evaluation](https://azendo.co/services/ai-engineering/)[Cloud and DevOps Infrastructure as code, CI/CD, observability](https://azendo.co/services/cloud-and-devops/)[MLOps engineering Deploying and monitoring models in production](https://azendo.co/services/mlops-engineering/) ## Managed data services for pipelines that already carry load Data work fails quietly. A pipeline that silently drops two per cent of rows produces a dashboard that is still confidently wrong, and nobody finds out until a decision has been made on it. Managed data services are assigned continuously for that reason: the value is in the monitoring and the quality gates, which are the first things to go unowned when data is somebody's second job. Where the problem is the warehouse model rather than the ingestion, an [analytics engineer](https://azendo.co/services/data-engineering/analytics-engineer/) is usually the right assignment. Where it is volume and scheduling across a lot of legacy sources, an [ETL developer](https://azendo.co/services/data-engineering/etl-developer/) is. The work happens inside your warehouse and your cloud account. We do not move your data to run it, and we do not put a layer of our own tooling between you and it. Where the pipelines exist to feed model-backed features rather than reporting, the same agreement frequently carries [ai engineering services](https://azendo.co/services/ai-engineering/) as well, on a single [committed monthly capacity](https://azendo.co/pricing/) rather than two engagements. ## Tell us what data engineering capacity your roadmap needs. Tell us about your project and the capacity you have in mind. A service delivery manager will get back to you.