What does a Data Engineer do at conDati?
conDati’s Data Engineers prepare and transform data using pipelines. This involves extracting data from various data source systems, transforming it into the staging area, and loading it into a data warehouse system ETL (Extract, Transform, Load). Data Engineers are responsible for building and maintaining data infrastructure, databases, and data pipelines (the design of systems for processing and storing data). They transform the data into a format that is useful for analysis.
This starts with cleaning, organizing, and processing raw, unstructured data. Then building the systems that capture, cleanse, transform and route data to destination systems, taking raw data from a SaaS platform such as a CRM system or email marketing tool and storing it so it can be analyzed using analytics and business intelligence tools.
- Data Engineers are responsible for finding and analyzing patterns in datasets. This requires transforming large amounts of data into formats that can be processed and analyzed. The role of a Data Engineer requires significant technical skills, including multiple programming languages and knowledge of SQL and AWS technologies. They are responsible for every step of data flow, from configuring data sources to managing analytical tools. In other words, they would architect, build and manage databases, data pipelines, and data warehouses.
- Data Engineers work side by side with data scientists to build tools they need to accomplish certain big data analytics goals. They oversee data integration tools that connect data sources to a data warehouse. These pipelines either simply transfer information from one place to another or carry out more specific tasks.
- Data Engineers focus on setting up and populating analytics databases, tuning them for fast analysis, and creating table schemas. This involves ETL (Extract, Transfer, Load) work.
- Work with internal and external stakeholders to assist with data-related technical issues and support data infrastructure needs.
- Data engineers need to be literate in programming languages used for statistical modeling and
analysis, data warehousing solutions, and building data pipelines, as well as possess a strong foundation
in software engineering:
- Database systems like SQL and NoSQL
- Data warehousing solutions
- ETL tools
- Machine learning
- Data APIs
- Python, Java, and Scala programming languages
- Cloud compute platform such as AWS
- Understanding the basics of distributed systems
- Knowledge of algorithms and data structures
- Communication skills
- Collaboration skills
- Presentation skills
BS in Computer Science or related fields or one plus years of direct experience
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