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Grow your data dream team with the right  talent

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We work with the best experts to look at your business goals and strategy, assess the current state
and look for a future state together.

Data Science & Business intelligence can help you take decisions, grow a business or optimize your processes beyond intuition and experience. However, you can get lost in all this information. You need a guide in this jungle. A light in the dark.

Why choose Dark Light? We work with the best experts in the market within data science, data engineering, data analytics & business intelligence. We look at the business goals and strategy, assess the current state and look for a future state together. Based on these conversations, we will provide you with the right consultant to implement the roadmap.

How can we help you

Data Analysis

Our Data Analysts gather, organize, and interpret statistical data using data analysis tools to produce meaningful results. The client then uses these interpretations to make important business decisions.
Skillset: SQL, NO SQL, Data cleaning, Tableau, PowerBI, Excel, Python

Data Engineering

Our data engineers prepare and manage big data that is then analyzed by data analysts and scientists. They are responsible for designing, building, integrating, and maintaining data from several sources
Skillset: Python, GCP, AWS, Azure, Cloudera, SQL Server, ETL, Spark, Kafka, Data Vault, Kimball

Data Science

Our data scientists analyze and interpret raw data into business solutions using machine learning and algorithms. Data science looks at the probability of future events and conditions. Predictive analysis uses historical data to forecast business trends, customer behavior, and product success. It seeks to answer questions about what will happen in the future. Prescriptive analysis seeks to find a solution to a specific business problem.
Skillset: Python, R, Machine Learning, SQL, noSQL, NLP, Hadoop, SAS, Spark

Data Management

Our data management help comprises all related to handling as a valuable resource, it is the practice of managing an organization’s data so it can be analyzed for
Skillset: Data Governance, Data Quality, Master Data Management, Data Architecture

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Business goals & strategy

Identifying business goals & objectives aligned to data


Current state assessment

Understanding current maturity & environment


Future state

Future state capabilities



Matching the right skillset
Change management



Follow-up Align with business goals

Which roles can we support?

Data Analysis

Data analysts deliver value to their companies by taking data, using it to answer questions, and communicating the results to help make business decisions. Common tasks done by data analysts include data cleaning, performing analysis and creating data visualizations.

Skills :

Excel, SQL, PowerBI, Tableau, Python, ...

The nature of the skills required will depend on the company’s specific needs, but these are some common tasks:

Cleaning and organizing raw data.

Using descriptive statistics to get a big-picture view of their data.

Analyzing interesting trends found in the data.

Creating visualizations and dashboards to help the company interpret and make decisions with the data.

Presenting the results of a technical analysis to business clients or internal teams.

Data Engineer

Data engineers build and optimize the systems that allow data scientists and analysts to perform their work. Every company depends on its data to be accurate and accessible to individuals who need to work with it. The data engineer ensures that any data is properly received, transformed, stored, and made accessible to other users

Skills :

Statistical machine learning & algorithms, stochastic processing, natural language processing,…

The data engineer’s mindset is often more focused on building and optimization. The following are examples of tasks that a data engineer might be working on:

Building APIs for data consumption.

Integrating external or new datasets into existing data pipelines.

Applying feature transformations for machine learning models on new data.

Continuously monitoring and testing the system to ensure optimized performance

Data Scientist

A data scientist is a specialist who applies their expertise in statistics and building machine learning models to make predictions and answer key business questions.

Skills :

Statistical machine learning & algorithms, stochastic processing, natural language processing,…

A data scientist still needs to be able to clean, analyze, and visualize data, just like a data analyst. However, a data scientist will have more depth and expertise in these skills and will also be able to train and optimize machine learning models:

Evaluating statistical models to determine the validity of analyses.

Using descriptive statistics to get a big-picture view of their data.

Testing and continuously improving the accuracy of machine learning models.

Business Intelligence

Our business intelligence experts use descriptive analysis to present historical data to business units in a way that makes it easy for them to visualize and understand. Business Intelligence is often used to generate reports that clearly and accurately communicate the current state of the business

Skills :

PowerBI, Tableau, SQL, Qlikview, programming skills, ...

They are responsible to build the data warehouse applications to support business intelligence requirements of a company. There are several niche roles within the umbrella of data warehousing engineers:

Data Architects to design the architecture for the data warehouse. They work along with other enterprise architects.

Data modellers to design the data model for the data warehouse, such that it is optimised for querying data.

Source system analysts to create data mappings from source system to data warehouse.

ETL developers to build ETL interfaces to load data into the data warehouse.

Business Intelligence developers to build reports and visualizations for business users.