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Data Engineer Job Description Boilerplates

Are you an HR-professional recruiting new members for your organisation’s data team? If you are looking for talent to fill a data engineer role, you have probably faced the challenge of writing a proper job description. Your CTO/Director of Engineering/Head of Data is very busy, and gave you only some high-level points, but nothing that fits into a JD. Don't worry, you are not alone.

Below you can find some boilerplate wording for the 'Tasks & Responsibilities' section of a data engineer job description. Adjust, adapt and complete it the way you see fit, the point is really to make you think less about the wording and more about finding the right person for the job.

One hint: be selective in what you write into the job ad, more does not equal better in this case.

Data engineer job description sample text

  • Work closely with the product team on the platform

  • Develop and scale the recommendation engine and comparable algorithms

  • Interface with the infrastructure team

  • Scale the architecture to manage increased traffic

  • Work on the backend structure, the API and partner integrations

  • Start integrating the recommendation engine

  • Measure and ensure data quality across the organisation

  • Use agile software development processes to deliver features

  • Build and document solid data pipelines

  • Clean, transform, and aggregate data from different sources

  • Develop and document processes for data mining, data modeling and data warehousing

  • Support building complex algorithms that provide business value to the customers

  • Ingest and aggregate data from both internal and external data sources

  • Help streamline the data science processes

  • Plan data models and architecture

  • Implement customer lifecycle and retention models based on an existing methodology

  • Employ an array of coding languages and tools to set up the data infrastructure

  • Interface with the reporting team and support their objectives with infrastructure tweaks

  • Execute/implement new data product features end-to-end

  • Engage with the machine learning team and uncover hidden efficiencies in the pipelines

  • Turn high-volume activity data into a highly accessible resource

  • Ingest and aggregate data from both internal and external data sources

  • Transform the available data into meaningful insights working with the analyst team

  • Develop simple models and integrate them with the visualisation tools

  • Work closely with the data science and business intelligence teams to develop data models and pipelines for research, reporting, and machine learning

  • Integrate state-of-the-art data management and software engineering technologies

  • Tap into new data streams from third-party APIs

  • Create custom software components for the data platform

  • Collaborate with the stakeholders in an interdisciplinary team

  • Research new approaches for making data accessible to customers

  • Connect legacy and new data systems together

  • Define basic tooling, metrics and other solutions and maintain quality to the highest standards

  • Write, extend and debug microservices for planned features

  • Collaborate with other engineers, data analysts, and product managers

  • Lead data strategy and inform the product strategy team on how world-class data experiences are built

  • Take leadership opportunities and shape the data culture within the organisation

  • Build near real-time and batch data processing pipelines

  • Design and optimise low latency systems

  • Build highly reliable but flexible service infrastructure

  • Maintain tooling and enable algorithms to move into production faster

  • Relate and match entities from different data streams

  • Increase the implementation speed of data tools

  • Creating secure processes to keep the data pipelines safe

  • Improve data models and foster data-driven decision making

  • Draw a comprehensive picture of user flows and enable deeper analysis

  • Specify data requirements and pre-processing routines

  • Hold companywide data trainings

  • Develop solutions for automatic labeling of data

  • Model front end and back end data sources

  • Design and optimise complex queries and deliver user value

  • Work closely with data scientists on modeling

  • Grow the data competency across the company