There is quite a wide variety of roles involved in data, some are more business oriented, some more engineering, some research and some are hybrids between the categories. I have previously touched based on a couple of these roles such as the data product owner, or data architect, but there is a much wider variety.

Business oriented Roles

1. Business Intelligence Analyst / Engineer: BI analyst and engineer, help surface business data. They work on setting up dashboards in tools such as Tableau / Domo or looker, create datasets used for deep dive and answer adhoc questions from the business. SQL is the main skill needed for the role, but data engineering knowledge, such of how to set up ETL pipelines using tools such as SSIS or in creating OLAP Cubes is beneficial.

2. Analytics & Strategic Planning Associate: is a role that is usually present in Tech Companies, sometimes the role is advertised as a “Financial analyst” role. The role entails defining Metrics, keeping track of performance against KPIs, benchmarking, as well as helping the business plan and prioritize based on expected result. Analytics & Planning associates typically work with excel and SQL queries to do their job as well as dashboarding tools such as PowerBI or Tableau.

3. Analytics Translators: help bridge the gap between the business and the analytics specialists. They help prioritize the work and serve as an interface between the different functions. They also work to handle certain project aspect of the data stream.

4. Insight Analyst: work alongside CRM marketing colleagues, where they may be responsible for the creation of segments, analyze campaign performance, work on setting up campaign A/B tests. They generally need a statistical background as they may need to do clustering, basket analysis or create propensity models. They usually work with SQL and statistical modelling tool such as SAS, R or SPSS.

5. Digital Analysts: have as main stakeholder the digital marketing teams. They try to optimize the performance of the overall digital sales and marketing effort through increasing the efficiency of marketing campaigns and flow the websites.

They work with clickstream data using analyze tools such as Google Analytics, Mixpannel, HotJar and A/B testing tools such as Google Optimize, Optimizely or Qubit.

They work with tag management systems, and sometimes javascript to implement tracking for google analytics and other platforms as well as provide integration guidelines for developers to implement the required attributes.They sometimes work with Data Management Platforms (DMP) and Customer Data Platforms (CDP), to leverage behavioural data for digital marketing purposes.

6. Operation Researcher: optimize business process using data. A business area that makes specific use of operation researchers is Supply Chain. There they help on scheduling, routing problems, optimize the network and improve production chains. They rely on statistical and optimization techniques to achieve results and leverage tools such as Fico Xpress, Matlab or Octave to achieve these goals.

Engineering Oriented Role

7. Data Engineers: Their core function is in the ingestion, structuring and standardization and processing of data. Their role has evolved quite a lot over the past few years and now involves dealing with both analytical and production systems.

They typically manage complex ETL or ELT pipelines, using advanced orchestration tool such as Airflow. Work with programming languages such as Python or Scala and work with a wide variety of data-store be them SQL or noSQL and working with data from data-stores, APIs alike or message brokers.

8. Machine Learning Engineers: are a very specialized type of backend software engineer focusing on leveraging data. They operationlize and put predictive models into production, building pipelines, api and training models.

Some of the work they do relates to creating recommender or image recognition systems. They can code in a wide variety of languages depending on the systems that they need to integrate with, and have knowledge of the typical Machine Learning libraries such as Spark MLib, XGBoost or Tensorflow.

9. Product analyst: help define the strategic direction for product development by pushing for data driven decision making. They are in charge of estimating the potential impact of the product components, analysing user behaviour data, working for the engineering team to setup and analyze A/B tests, setup goals and tracking the performance against these goals. Their toolset mainly consists of SQL and Dashboarding tools, but may involve more advanced technologies or leveraging tools such as google analytics.

10. Data Product Owner: help organize the overall direction of products leveraging data. They help define the different requirements, own the product roadmap, liaise with stakeholders as well as tackle some project management tasks, such coordinating the acquisition of the necessary training data for a Machine Learning project.

11. Data Vizualization Engineer: make data looks beautiful. They typically work as part of a product team setting up dashboard within applications. They typically leverage Javascript for this purpose, using libraries such as D3js, VisGL and leafletJS.

12. Data Ops: help setup the data infrastructure, help handle code deployment, setup data quality checks and alerting & monitoring for the different flows.

Research Oriented Role

13. Research Scientist: deep dives complicated problems, which may or may not have a direct business outcome. Think of them as sowing the seed to what will become future products. Think of image classification before it became mainstream and implemented in products, or people working on testing new deep neural network architectures.

14. Statisticians: can help answer more complex questions with statistical rigour. This is the case in biostatistics where statisticians can help in designing and analyzing clinical trials and help provide the regulatory submissions.

15. Economists: work on leveraging data and economic theory for the good of companies. That can involve designing auction systems for ads, or working on predicting economic trends for risk assessment and capital allocation.

Hybrid & Other roles

16. Data Scientist: Data-scientists roles can be quite varied, cover a wide range of skills and depending on the specific position, might end up on different parts of the organization. They can cover knowledge of R, Python, Scala, Javascript and C++ along with SQL and noSQL knowledge. Some role might require knowledge of cloud based deployments, while other would be focused more on presentation skills.

17. DataBase Administrator: Database administrators are responsible for the setup and health of database. They are responsible for the planning and archiving of backups of the database, for monitoring and optimizing the performance of the database and the queries that are performed on top of it.

18. Data Architect: Lead the overall architectural setup of projects involving data. They can act as Technical product manager with regards as to what gets built, as well as choose the appropriate technology stack and data structure organization.

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