Hiring Requirement

When setting up a large-scale recruiting process, it’s important to focus on hiring experienced engineers and talent who can boost the company’s overall know-how or fill skill gaps. The competitive hiring environment makes it hard to find qualified candidates, since there may be only a few people who meet the criteria and many companies compete for the same talent pool. Treat the hiring pipeline like a sales funnel and continuously measure its “temperature” to ensure efficient and successful hiring efforts. Without constant monitoring, time gets wasted and inefficiencies arise in the recruitment team. Success in large-scale hiring relies on the team’s ability to fill key roles quickly and spot and remove bottlenecks to optimize workflow. Experimenting with different job descriptions for the same role across various channels can help find which approach brings more quality candidates into the pipeline. This, however, adds extra work for an already busy hiring team. Setting firm deadlines for candidates to complete each phase of the hiring process and tracking hires per week helps keep a steady pace and clearer forecasts for future hiring needs. To support a business plan and hire at scale, you need a well-defined career development system to assess candidates during hiring. This enables more objective evaluation of potential hires and ensures you bring on the right people to meet the company’s needs. Balancing your hiring efforts and keeping a steady number of hires per week lets the team manage the process better and plan future needs. Ultimately, by continuously measuring and improving the speed and efficiency of the hiring pipeline, companies can increase their chances of building a strong, talented team.

The main goal of the “Building a Data-Driven Hiring Machine” project is to establish a data-driven hiring process to recruit experienced engineers and talent, boost the company’s overall know-how, and fill skill gaps to ensure efficient and successful hiring efforts.

• Introduce a data-driven hiring process for experienced engineers and talent.
• Improve the company’s overall competency and fill skill gaps.
• Continuously measure the hiring pipeline’s “temperature.”
• Identify and remove bottlenecks in the hiring process to optimize workflow.
• Experiment with different job descriptions for the same role across various channels.
• Set deadlines for candidates to complete each hiring phase.
• Track weekly hires to maintain a steady pace and clear forecasts for future hiring needs.
• Implement a well-defined career development system to evaluate candidates during the hiring process.
• Ensure balanced hiring efforts and maintain a consistent number of hires per week.
• Continuously measure and improve the speed and efficiency of the hiring pipeline.

There’s a need for a founding engineer experienced in Ethereum and smart contracts for a startup. The company wants someone with solid computer science fundamentals and problem-solving skills, along with knowledge of Ethereum, smart contracts, Solidity, Golang, and other programming languages. But when a junior recruiter posts the role, they struggle to identify the specific requirements and find suitable candidates. This often requires intervention from an HR manager or senior developer to refine the job post and provide details so the junior recruiter can search the company’s resume database effectively. To solve this, the company plans to introduce an AI-based hiring platform using semantic search. Semantic search aims to improve accuracy by understanding the searcher’s intent and the contextual meaning of terms in the searchable data space. Here, the goal is to capture the job post’s intent and match it with relevant resumes from the company’s database. Before matching resumes, the company must capture the contextual meaning of all resumes and store it in a suitable format for queries. To extract the resumes’ contextual meaning, the company plans to use the HuggingFace Transformers library, known for its NLP capabilities. The library generates embedding vectors—multi-dimensional float vectors representing the contextual meaning of sentences, words, or paragraphs. These vectors, which can have hundreds to thousands of dimensions, help match the job post with resumes in the database.

Technologies:

• Delphi and Microfocus Cobol.

• Reviewed OPC UA, Redis, Influx.
• Created an as-is analysis of data storage, processing, streaming, and file format requirements for IQ control, service info, manufacturing data, and axis monitoring use cases.
• Advised the company on suitable databases for each use case.
• Improved efficiency by delivering actionable recommendations.
• Implemented Kafka IoT streaming.
• Managed data in Azure Data Factory for analysis.

The main task of this project was to analyze and optimize data storage, processing, and streaming for various use cases, advise on fitting databases, and boost efficiency using IoT streaming and Azure Data Factory for analytics.

Responsibilities:

• Analyzed and optimized data storage, processing, and streaming for various use cases.
• Advised on fitting databases to improve efficiency.
• Leveraged IoT streaming and Azure Data Factory for analytics.
• Reviewed OPC UA, Redis, and Influx.
• Performed a requirements analysis on data storage, processing, streaming, and file formats for IQ control, service info, manufacturing data, and axis monitoring.
• Provided database recommendations for each use case.
• Enhanced efficiency with actionable recommendations.
• Implemented Kafka IoT streaming.
• Managed data in Azure Data Factory for analysis.

Technologies:

• Kafka Docker
• Cassandra DataStax
• Python scripting
• Graph and Spark
• Ansible
• Grafana
• Prometheus
• Azure Data Factory

• Evaluate and predict tenant applicants before rental contract allocation.
• Create statistical insights using classic clustering.
• Prepare and use tenant data to build predictions with Random Forest.
• Reporting with Power BI.
• Visualization with Bokeh and Dash.
• Lead BI development with Power BI and Tableau.
• Migrate the application to GCP platform.
• Build a Delta Lakehouse.
• Work with mostly unstructured data.
• Maintain RDB data, scale and manage with Kubernetes.

Other details:

• Successfully reduced tenant turnover through improved clustering.
• Used SAP HANA Community Edition as the database.
• Used Kafka for communication.
• Used Data Factory for further processing.
• Added a Delta Lakehouse for persistent data storage.

Responsibilities:

• Evaluate potential tenants before rental contracts.
• Create statistical predictions using classic clustering.
• Prepare and use tenant data to build Random Forest predictions.
• Report using Power BI.
• Visualize data with Bokeh and Dash.
• Lead BI development with Power BI and Tableau.
• Migrate the application to GCP platform.
• Build a Delta Lakehouse.
• Analyze mostly unstructured data.
• Maintain RDB data.
• Scale and manage with Kubernetes.

Technologies:

• Kafka
• Python
• Databricks DataStax
• OpenStack basics
• OpenCV and OpenAI
• PyTorch
• TensorFlow
• Keras
• Pandas
• NumPy
• Tableau
• Microsoft Power BI
• Data Factory

• Developed municipal administration software using Microfocus Cobol, Sybase ASE, SUSE Linux.
• Led the public administration migration project from z/OS to Azure in 2022.
• A step-by-step migration was planned to move solutions from a mainframe to Azure.
• We first moved some applications, while others stayed temporarily or permanently on the mainframe.
• This approach usually requires systems to enable interoperability of applications and databases between the mainframe and Azure.
• Fortunately, there are many solutions that integrate Azure with existing mainframe environments.

The main task of this project was to develop municipal administration software using Microfocus Cobol, Sybase ASE, and SUSE Linux, and to plan a phased migration of solutions from a mainframe to Azure, ensuring interoperability of applications and databases.

Responsibilities:

• Develop municipal administration software with Microfocus Cobol, Sybase ASE, and SUSE Linux.
• Lead the public administration migration project from z/OS to Azure in 2022.
• Plan and implement a phased migration of solutions from a mainframe to Azure.
• Ensure some applications remain temporarily or permanently on the mainframe.
• Set up systems to guarantee interoperability of apps and databases between the mainframe and Azure.
• Use a range of solutions to integrate Azure with existing mainframe environments.
• Serve as developer, architect, and project manager.
• Use skills in Delphi and Microfocus Cobol.

Technologies:

• Delphi and Microfocus Cobol