Data Engineering for Startups
Startups don’t need a complex data engineering stack. How to prioritize connectors, analytics destinations, and transformations by business stage.
Do you know what Data Engineering is and how it can help your startup scale quickly and with the highest possible quality? In this article, we explain the impact of data engineering on business growth, and how to prioritize connectors, analytical destinations, and transformations according to your company's stage, instead of trying to build everything at once.
What is Data Engineering?
Usually, when we talk about startup data infrastructure, it is rarely well-structured from the beginning, which is natural in dynamic environments where many things need to be bootstrapped and executed with agility to boost the business. For this reason, in this type of organization, there is an initial effort to organize and map all of the company's data sources, ingest this data, and, after the ingestion process, store it following a treatment and modeling logic, such as the layer pattern used in a Medallion Architecture, for example.
In an ideal scenario, these processes should be automated and easy to access in real-time. The main idea of this process is to get the house in order and make data useful for users and systems, rather than something centralized in specific departments or roles. In the data field, there is what we call Data Engineering precisely to solve these issues.
Why is data important for a Startup?
Being Data-Driven is about making decisions based on empirical evidence, using the data collected throughout the organization's activities to guide the next steps effectively.
When a company is in its early stages, especially when it is focused on developing one or more products—as is the case with startups—it is extremely necessary to build a culture of constant innovation.
In this context, we often hear about "failing fast, learning fast, and succeeding fast," which is one of the most important cultural aspects for companies seeking accelerated growth. Having an adequate strategy to transform into a data-driven company ends up being a major turning point.
Key questions for implementing efficient Data Engineering:
What tools are needed to make this a reality?
How do we understand that something is not working without efficient metrics that indicate it?
How do we learn from mistakes without agile tracking of what was implemented?
How do we know if a success is truly a success without a reliable benchmark to understand the process evolution?
These questions are important to understand the best way to build a data-driven culture. It all starts with a good data engineering process within the company, and for startups, this is fundamental.
What you need to leverage Data Engineering in your startup
There are several important implementations to leverage your startup's data engineering, but the following are excellent starting points.
Modern Data Stack
The Modern Data Stack (MDS) is a set of six data components used to achieve a specific end result. Each component can be a set of different technologies working together so that an organization's data is well handled, easily accessible by everyone, and, in the end, can be interpreted more efficiently, enabling a data-driven organization.
1) Data source
Today, companies rely on several different platforms for each part of their organizational processes: CRM for lead management and sales processes; helpdesks for Customer Success; as well as platforms for employee experience management, financial management, and logistics. Each tool generates its own set of data and insights, and these need to be organized in a way that makes sense and is easy to access.
In the Modern Data Stack, this is achieved through a storage architecture where, after the ingestion process, the treated data is stored. These structures can be a Data Warehouse, a Data Lake, or even a Data Lakehouse.
2) Ingestion
Data ingestion is about exporting and processing the company's data so that it is stored in a standardized way and accessed as needed within the storage architecture used. In this process, there are two very important technical concepts: ETL or ELT.
In basic terms, each letter refers to steps used for data ingestion: Extraction is the process of obtaining the data, Transformation is adapting it to the format used by your MDS, and Loading is uploading the data into the storage architecture.
The difference between ETL and ELT lies in the order of the steps. In the former, after extraction, data is transformed before being loaded. In the latter, data is loaded first into the storage system to be transformed later as needed. Early-stage startups usually benefit more from ELT precisely because it postpones the modeling decision: you load raw data first and decide how to transform it as business questions arise, instead of modeling upfront something that might still change.
3) Transformation
After data reaches the storage architecture (whether through ETL or ELT), it is still, in most cases, in the raw format of the original source. The transformation stage is where this raw data is cleaned, standardized, and organized into tables that make sense for analysis: removing duplicates, standardizing types, joining related tables, and calculating business metrics from the raw data.
This is where transformation tools like dbt come in, allowing you to version control this logic as code, test transformations before publishing them, and document what each transformed table represents. Without this well-defined stage, the same raw data ends up being interpreted in different ways by different people on the team, each calculating their own version of a metric like "monthly revenue" or "churn."
4) Modeling
Data modeling tools serve to take treated data from your storage architecture and convert it into formats that are more accessible and interpretable, usually organized in layers of progressive quality, as proposed by the Medallion Architecture: raw data, processed data, and business-ready data.
5) Visualization
This component of the Modern Data Stack is no longer about data engineering, but rather visualization and data application. This is where BI tools sit, which are extremely important for understanding data, as they transform raw data into charts, tables, and dashboards that enable faster and more assertive analysis.
6) Activation
This process operationalizes data, pulling it through your stack and allowing value to be extracted from it in real-time, usually by sending modeled data back to operational tools (such as sending a lead score to the CRM, or a high-risk churn list to the CS tool).
How to prioritize each component by startup stage
Not every startup needs all six components at the same time, and trying to implement everything at once is the most common mistake made by those starting out. The priority order changes according to the business stage.
Early stage (few data sources, small team). Prioritize simple ingestion from two or three sources that already concentrate most of the business decisions (usually CRM, product, and financial), loaded via ELT directly into an accessible warehouse like BigQuery or PostgreSQL. Modeling and transformation can be minimal at this stage: the data volume is still small enough that direct queries on raw tables can solve most questions.
Growth stage (more sources, first data hires). This is where it pays off to invest in a managed connector instead of custom scripts for every new source, because the number of integrations grows faster than the team can manually maintain. It is also the time to formalize the transformation stage with a tool like dbt, so that different people on the team stop calculating the same metric in different ways. This is exactly where Brick, an insurtech, was before centralizing ingestion: the co-founder was manually maintaining pipelines with Airflow, interrupting his own product work about twice a week to fix schema breaks. After centralizing ingestion, the company started maintaining its entire data infrastructure with a single analyst.
Mature stage (multiple teams consuming data, need for governance). Here, the priority shifts to observability, layered architecture (Bronze, Silver, Gold), and activation, returning modeled data to daily operational tools. At this stage, ingestion maintenance engineering (pagination, rate limit, schema evolution) should already be completely outsourced, so the team can focus on modeling and governance, not plumbing.
Count on the right people
Like any complex change process within an organization, being able to count on the right people is key. This is even more striking when we think of startups, where the same analyst often wears multiple hats of engineering, modeling, and analysis at the same time.
For this reason, companies like Erathos are essential players in helping your company start or proceed with its data-driven journey, taking care of the ingestion part (component 2 of the Modern Data Stack) so that your team can focus on transformation, modeling, and activation.
Frequently asked questions about data engineering for startups
When should a startup start investing in data engineering? As soon as business decisions start to depend on numbers coming from more than one tool (for example, combining CRM data with product data). Before that, spreadsheets and native reports from the tools themselves are usually enough.
ETL or ELT, which one should a startup choose first? In most cases, ELT. It postpones the modeling decision, which is an advantage when the business is still figuring out what questions to ask the data. Traditional ETL requires deciding on modeling before loading, which locks up flexibility precisely when the startup needs it most.
Do I need a hired data engineer from day one? Not necessarily. With a managed connector handling ingestion and a transformation tool like dbt, a data analyst or even a technical founder can run the operation solo for quite a while before needing a dedicated hire.
What is the difference between a Data Warehouse, a Data Lake, and a Data Lakehouse? A Data Warehouse stores structured data optimized for fast analytical queries. A Data Lake stores raw data in any format, structured or unstructured—usually cheaper but less optimized for querying. A Data Lakehouse combines both, organizing raw data into progressive quality layers within the same environment.
Conclusion
Every organization's data-driven journey begins with solid data engineering. For startups, this is a fundamental need:
Data-driven innovation is more assertive and minimizes operational errors, ensuring precise analysis and more realistic forecasts to guide action plans.
Data engineering gets the house in order and makes information flow more freely across different departments and hierarchical levels. For startups, this ensures accelerated value generation, with increasingly objective processes and decisions focused on growth.
Not having a good data engineering strategic partner can increase decision-makers' reaction time and allow small errors to go unnoticed, wasting valuable resources.
Want to see how this works in practice? See how Brick swapped manual data ingestion for a data-driven operation, or discover Erathos' platform and see how to handle data ingestion without needing to hire a data engineering team from the start.
Do you know what Data Engineering is and how it can help your startup scale quickly and with the highest possible quality? In this article, we explain the impact of data engineering on business growth, and how to prioritize connectors, analytical destinations, and transformations according to your company's stage, instead of trying to build everything at once.
What is Data Engineering?
Usually, when we talk about startup data infrastructure, it is rarely well-structured from the beginning, which is natural in dynamic environments where many things need to be bootstrapped and executed with agility to boost the business. For this reason, in this type of organization, there is an initial effort to organize and map all of the company's data sources, ingest this data, and, after the ingestion process, store it following a treatment and modeling logic, such as the layer pattern used in a Medallion Architecture, for example.
In an ideal scenario, these processes should be automated and easy to access in real-time. The main idea of this process is to get the house in order and make data useful for users and systems, rather than something centralized in specific departments or roles. In the data field, there is what we call Data Engineering precisely to solve these issues.
Why is data important for a Startup?
Being Data-Driven is about making decisions based on empirical evidence, using the data collected throughout the organization's activities to guide the next steps effectively.
When a company is in its early stages, especially when it is focused on developing one or more products—as is the case with startups—it is extremely necessary to build a culture of constant innovation.
In this context, we often hear about "failing fast, learning fast, and succeeding fast," which is one of the most important cultural aspects for companies seeking accelerated growth. Having an adequate strategy to transform into a data-driven company ends up being a major turning point.
Key questions for implementing efficient Data Engineering:
What tools are needed to make this a reality?
How do we understand that something is not working without efficient metrics that indicate it?
How do we learn from mistakes without agile tracking of what was implemented?
How do we know if a success is truly a success without a reliable benchmark to understand the process evolution?
These questions are important to understand the best way to build a data-driven culture. It all starts with a good data engineering process within the company, and for startups, this is fundamental.
What you need to leverage Data Engineering in your startup
There are several important implementations to leverage your startup's data engineering, but the following are excellent starting points.
Modern Data Stack
The Modern Data Stack (MDS) is a set of six data components used to achieve a specific end result. Each component can be a set of different technologies working together so that an organization's data is well handled, easily accessible by everyone, and, in the end, can be interpreted more efficiently, enabling a data-driven organization.
1) Data source
Today, companies rely on several different platforms for each part of their organizational processes: CRM for lead management and sales processes; helpdesks for Customer Success; as well as platforms for employee experience management, financial management, and logistics. Each tool generates its own set of data and insights, and these need to be organized in a way that makes sense and is easy to access.
In the Modern Data Stack, this is achieved through a storage architecture where, after the ingestion process, the treated data is stored. These structures can be a Data Warehouse, a Data Lake, or even a Data Lakehouse.
2) Ingestion
Data ingestion is about exporting and processing the company's data so that it is stored in a standardized way and accessed as needed within the storage architecture used. In this process, there are two very important technical concepts: ETL or ELT.
In basic terms, each letter refers to steps used for data ingestion: Extraction is the process of obtaining the data, Transformation is adapting it to the format used by your MDS, and Loading is uploading the data into the storage architecture.
The difference between ETL and ELT lies in the order of the steps. In the former, after extraction, data is transformed before being loaded. In the latter, data is loaded first into the storage system to be transformed later as needed. Early-stage startups usually benefit more from ELT precisely because it postpones the modeling decision: you load raw data first and decide how to transform it as business questions arise, instead of modeling upfront something that might still change.
3) Transformation
After data reaches the storage architecture (whether through ETL or ELT), it is still, in most cases, in the raw format of the original source. The transformation stage is where this raw data is cleaned, standardized, and organized into tables that make sense for analysis: removing duplicates, standardizing types, joining related tables, and calculating business metrics from the raw data.
This is where transformation tools like dbt come in, allowing you to version control this logic as code, test transformations before publishing them, and document what each transformed table represents. Without this well-defined stage, the same raw data ends up being interpreted in different ways by different people on the team, each calculating their own version of a metric like "monthly revenue" or "churn."
4) Modeling
Data modeling tools serve to take treated data from your storage architecture and convert it into formats that are more accessible and interpretable, usually organized in layers of progressive quality, as proposed by the Medallion Architecture: raw data, processed data, and business-ready data.
5) Visualization
This component of the Modern Data Stack is no longer about data engineering, but rather visualization and data application. This is where BI tools sit, which are extremely important for understanding data, as they transform raw data into charts, tables, and dashboards that enable faster and more assertive analysis.
6) Activation
This process operationalizes data, pulling it through your stack and allowing value to be extracted from it in real-time, usually by sending modeled data back to operational tools (such as sending a lead score to the CRM, or a high-risk churn list to the CS tool).
How to prioritize each component by startup stage
Not every startup needs all six components at the same time, and trying to implement everything at once is the most common mistake made by those starting out. The priority order changes according to the business stage.
Early stage (few data sources, small team). Prioritize simple ingestion from two or three sources that already concentrate most of the business decisions (usually CRM, product, and financial), loaded via ELT directly into an accessible warehouse like BigQuery or PostgreSQL. Modeling and transformation can be minimal at this stage: the data volume is still small enough that direct queries on raw tables can solve most questions.
Growth stage (more sources, first data hires). This is where it pays off to invest in a managed connector instead of custom scripts for every new source, because the number of integrations grows faster than the team can manually maintain. It is also the time to formalize the transformation stage with a tool like dbt, so that different people on the team stop calculating the same metric in different ways. This is exactly where Brick, an insurtech, was before centralizing ingestion: the co-founder was manually maintaining pipelines with Airflow, interrupting his own product work about twice a week to fix schema breaks. After centralizing ingestion, the company started maintaining its entire data infrastructure with a single analyst.
Mature stage (multiple teams consuming data, need for governance). Here, the priority shifts to observability, layered architecture (Bronze, Silver, Gold), and activation, returning modeled data to daily operational tools. At this stage, ingestion maintenance engineering (pagination, rate limit, schema evolution) should already be completely outsourced, so the team can focus on modeling and governance, not plumbing.
Count on the right people
Like any complex change process within an organization, being able to count on the right people is key. This is even more striking when we think of startups, where the same analyst often wears multiple hats of engineering, modeling, and analysis at the same time.
For this reason, companies like Erathos are essential players in helping your company start or proceed with its data-driven journey, taking care of the ingestion part (component 2 of the Modern Data Stack) so that your team can focus on transformation, modeling, and activation.
Frequently asked questions about data engineering for startups
When should a startup start investing in data engineering? As soon as business decisions start to depend on numbers coming from more than one tool (for example, combining CRM data with product data). Before that, spreadsheets and native reports from the tools themselves are usually enough.
ETL or ELT, which one should a startup choose first? In most cases, ELT. It postpones the modeling decision, which is an advantage when the business is still figuring out what questions to ask the data. Traditional ETL requires deciding on modeling before loading, which locks up flexibility precisely when the startup needs it most.
Do I need a hired data engineer from day one? Not necessarily. With a managed connector handling ingestion and a transformation tool like dbt, a data analyst or even a technical founder can run the operation solo for quite a while before needing a dedicated hire.
What is the difference between a Data Warehouse, a Data Lake, and a Data Lakehouse? A Data Warehouse stores structured data optimized for fast analytical queries. A Data Lake stores raw data in any format, structured or unstructured—usually cheaper but less optimized for querying. A Data Lakehouse combines both, organizing raw data into progressive quality layers within the same environment.
Conclusion
Every organization's data-driven journey begins with solid data engineering. For startups, this is a fundamental need:
Data-driven innovation is more assertive and minimizes operational errors, ensuring precise analysis and more realistic forecasts to guide action plans.
Data engineering gets the house in order and makes information flow more freely across different departments and hierarchical levels. For startups, this ensures accelerated value generation, with increasingly objective processes and decisions focused on growth.
Not having a good data engineering strategic partner can increase decision-makers' reaction time and allow small errors to go unnoticed, wasting valuable resources.
Want to see how this works in practice? See how Brick swapped manual data ingestion for a data-driven operation, or discover Erathos' platform and see how to handle data ingestion without needing to hire a data engineering team from the start.