Data Engineer Overview
The Role: A Data Engineer is a professional who designs, constructs, and maintains systems that collect, store, and analyze large amounts of data. Their role involves ensuring that data is accessible, reliable, and efficient to support organizational decision-making, analytics, and business operations. They are responsible for creating the infrastructure that allows data scientists and analysts to work with data effectively. How the role fits within a team: What sort of people and skills do I need in my team? More details on requirements and responsibilities: Data Engineer Job Specification Examples of what a Data Engineer does day-to-day: A day in the life of... an Academy Trainee A Day in the Life of a... Data Engineer How to Become a Data Engineer: Learn more about the experiences and qualifications needed to become a Data Engineer and progress through the levels. How I Became a... Data Engineer Recommended Quantexa Certifications: The Quantexa Academy is a modular learning experience that teaches individuals the key skills needed to successfully develop, deploy, and use the Quantexa Platform. It offers a range of courses and programs. The technical certification programs (Academies) offer assessments that allow learners to gain Quantexa Certifications. We recommend Data Engineers in your organisation gain the following certifications to deploy and maintain Quantexa. Quantexa Data Engineer Academy (DEA): The Quantexa Data Engineer Academy is an in-depth program aimed at teaching Data Engineers how to build Quantexa Platforms. Quantexa Task Loading (QTL): An in-depth program designed to help Quantexa Certified Data Engineers implement and customize Task Loading on a project Quantexa Scoring Engineer Academy (SEA): The Quantexa Scoring Engineer Academy qualifies the individual to write key detection logic that can be implemented within the Quantexa Assess framework. Top Community Areas: Quantexa Platform Support Developer Guides Community Training Program Product Research ProgramSenior Data Engineer Job Specification
This job specification highlights the core activities and skills essential for Senior Data Engineers working with the Quantexa Platform. We recommend that Senior Data Engineers have at least four years of industry experience as a Data Engineer or in an equivalent role. For Senior Data Engineers working with the Quantexa Platform, the tools, responsibilities, and expertise recommended to meet industry standards are outlined. For Hiring Managers, it serves as a framework for defining role requirements when recruiting candidates with Quantexa experience. Important Note: These guidelines are for reference only, and companies may have additional or specific expectations for roles. Day-to-Day Activities Writing efficient, fault-tolerant, production-level code for your deployment. Building and managing end-to-end data pipelines. Advising colleagues on technical concepts and translating them into actionable insights for decision-making. Collaborating to develop and implement solutions for complex challenges. → Modified Mentoring junior team members and collaborating with stakeholders to develop and implement solutions, ensuring best practices are followed. Recommended Quantexa Skills & Experience Experience implementing any of the Quantexa modules. Technical & business understanding of how and why Quantexa solutions are implemented. Comfortable performing most of the Quantexa standard tasks, such as: Parsers tuning Entity Resolution Tuning Complex Scoring Decision Systems (previously Detection Packs) Data Packs Upgrades Entity Reporting Authentication and Authorization Recommended Technical Skills Experience independently shaping and executing solutions for initiatives and challenges. Ability to persist in the face of roadblocks, dispatching them efficiently, and escalating when required. Advanced Scala, Spark, and Git. Proficiency in programming languages, particularly Scala, Java, Python, or similar. Experience in building and deploying production-level data processing systems. Experience with modern development tools and processes (e.g., Git, Gradle, Nexus) and automation/DevOps technologies (e.g., Jenkins, Docker, Bash scripting). Knowledge of cloud platforms (Google Cloud, Azure, AWS) and big data technologies (Spark, Hadoop, Elasticsearch). Understanding of testing frameworks and the ability to write unit and integration tests using libraries like ScalaTest. Recommended Soft Skills Experience supporting or mentoring junior members within a team. Ability to identify and highlight risks, communicating them early; not just in your own work, but also what you notice in others’ work and during backlog refinement. Experience leading agile ceremonies. Ability to step in and cover Technical Leads (TLs) where needed with a strong understanding of their priorities and project plans. Problem-solving mindset with the ability to tackle complex challenges. Strong communication skills for articulating technical concepts. Adaptability to work in rapidly changing client environments. Read more about Senior Data Engineers.Data Engineer Job Specification
This job specification highlights the core activities and skills essential for Data Engineers working with the Quantexa Platform. For Data Engineers working with the Quantexa Platform, the tools, responsibilities, and expertise recommended to meet industry standards are outlined. For hiring managers, it serves as a framework for defining role requirements when recruiting candidates with Quantexa experience. Important Note: These guidelines are for reference only, companies may have additional or specific expectations for roles. Equivalent Roles: • Software Engineer • Engineer II Day-to-Day Activities: Writing efficient, fault-tolerant, production-level code for your deployment. Building and managing end-to-end data pipelines. Collaborating to develop and implement solutions for complex challenges. Advising colleagues on technical concepts and translating them into actionable insights for decision-making. Recommended Quantexa Skills & Experience: Experience deploying Quantexa modules, preferably with more complex ETL pipelines and scores. Experience managing tasks such as: Graph Scripting Task Loading Delta ETL Aggregated data model Generating test data DQ tools Setting up Data Viewer/ Explorer Batch Resolver Participation in sprint estimation, work planning, and retrospective activities. Recommended Technical Skills: Proficiency in programming languages, particularly Scala, Java, Python, or similar. Experience in building and deploying production-level data processing systems. Experience with modern development tools and processes (e.g., Git, Gradle, Nexus) and automation/DevOps technologies (e.g., Jenkins, Docker, Bash scripting). Knowledge of cloud platforms (Google Cloud, Azure, AWS) and big data technologies (Spark, Hadoop, Elasticsearch). Understanding of testing frameworks and the ability to write unit and integration tests using libraries like ScalaTest. Ability to raise issues and risks, debug, and provide evidence of a root cause analysis. Recommended Soft Skills: Problem-solving mindset with the ability to tackle complex challenges. Strong communication skills for articulating technical concepts. Adaptability to work in rapidly changing client environments. Ability to manage multiple tasks, communicate updates to your team, and deliver commitments on schedule. Read more about Data Engineers.
