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Hiring a Freelance Data Engineer

Why and when you need a Data Engineer?

So we have a data team, and capabilities to run analysis and come up with data driven improvements in our company….that is already a big deal in regards to data maturity. However, new use cases and applications must be produced and our platforms must allow scalability, quality and reliability when using data. Here is where a data engineer comes into the scene!

Data engineers will make sure that your platforms, infrastructure and set up is working at an optimal level and that your increasing data driven processes run smoothly and in time.

As companies aim to scale up, they often encounter that most of their processes are deemed, poised and not automated, yet, still only 55% of companies believe in its power. This shift we are experiencing towards automation is being triggered at scale throughout the world, being data engineers one of the most demanded roles in the market.

With the best engineering practices surrounding our data, not only we accelerate the delivery of services to clients, we are able to increase its quality resulting in more efficient business, gaining flexibility and scalability which in turn gives more room to make distinctive insights emerge, which results boosting our productivity and savings. It’s a nurturing feedback loop.

As reported at 2020 Dice Tech Job Report, Data Engineer is the fastest growing job in the USA and stands out by a significant margin.

Companies can reach out to freelance data engineers and architects that can run audits, propose action plans and roll them out to ensure that your data related operations run smooth, are reliable and accessible to the business.

Through Outvise, you can find skilled and certified data engineer freelancers or consultants and pay only for the services delivered.

Certified Data engineers in the Network

case study
Data engineer use case for a mid-sized renewable energies company

Challenge, Context, Problems to be solved

Based in the UK, the company offers commercial energy assessment across the south of the country in the form of thermal modelling, energy performance certificates, SAP calculations, display energy certificates and more.

The need for automating a lot of processes became clear upfront. Out of the many areas in which engineering around data was necessary, we focused on delivering at one particular target, namely, enhancing their thermal modelling analysis and overheating assessments.

Mission, tools and methodology

Candidates for “digitalization” were both business operations and analytics. On one hand there is part of the business operations that begged for a change. Whereas in the recent past their clients would send project documentation via e-mail, we opted for choosing Google suite, Google sheets and Google apps script for automating the whole deal of project documentation upload.

Delivered eventually as a web app and powered by Google apps script which is written in modern Javascript, clients are prompted to upload a series of documents via Google sheets. Custom functions are then executed, carrying out a load of the data dynamically into the clients master Google drive.

Secondly, in the analytics front, the company relies heavily on IESVE software, an in depth suite of integrated analysis tools which, by the time the data engineering integration started, lacked a considerable number of features.

After clients upload the corresponding project documents, their data architects, design and run 3D dynamic simulation modeling, generating results and project data then available via an API. However, the enterprise still missed important granularity and solicited more dimensions and new features to be engineered. Tackling each new feature separately allowed us to familiarize ourselves in depth with the depth and breadth of IESVE's API and account thoroughly for all the caveats, particularities, possible shortcomings of such application and to accommodate our resources accordingly.

Upon its completion, we seeked further for the expansion of their analysis, namely, we laid out and came up with new attributes, to be then turned into new features, the calculation of which and its underlying logic then transferred to a set of ETL pipelines resuming all possible combinations of transformation logic into a bank where each of the pipelines gets triggered according to a set of rules curated hand in hand with architects.

Fetching data from both IESVE API and retrieving those computed data points and config parameters for each type of thermal modelling then ensured a successful trigger of ETL logic.

Last, at the right end, down the pipeline, right past the ETL bank we end up with a reporting facility which is in charge of formatting data to suit specific design aesthetics in accordance with various layouts proposed by the company.

Achieved results

For the time this project lasted, an approximate of a month and a half, became crystal clear the power that data engineering and automation brings to clients, easing up work and cutting times in business, operations and analytics, and when accurately assessed, a surplus of new and exciting opportunities arise, not only for full-fledged growth, but for a more nuanced view and understanding of what you can do with data.

7 weeks

Must have Data Engineer Skills

Such relevant profiles must master the following capabilities:

  • Build, Parameterize, Maintain data structures and databases.
  • Design data processing systems and organizations
  • Analyze Data and enable Machine Learning.
  • Design for reliability.
  • Visualize data and advocate policy.
  • Model business processes for analysis.
  • Design for security and compliance.

Other specific must have data engineer skills:

  • Debugging and problem solving.
  • ETL tools and software.
  • Cloud data engineering skills – AWS, GCP, Azure, IBM (2+)
  • Other skills : Hive, Apache Spark, Mongodb, MySQL, PostgreSQL.