Discover and apply for Data Engineer/Data Modeler jobs, a pivotal role at the intersection of data architecture and infrastructure. Professionals in this hybrid position are the master builders of the data world, responsible for designing the blueprints (data models) and constructing the robust pipelines (data engineering) that transform raw information into a structured, reliable, and accessible asset for organizations. These experts ensure that data flows seamlessly from source to consumption, enabling analytics, business intelligence, and data-driven decision-making. A Data Engineer/Data Modeler typically begins by analyzing business requirements to design conceptual, logical, and physical data models. This involves defining tables, columns, relationships, and constraints to ensure data integrity, efficiency, and alignment with organizational goals. They employ modeling methodologies like dimensional modeling for data warehouses, data vault for agile and historical data integration, or normalized models for transactional systems. Following the design phase, they engineer the practical implementation. This includes building and maintaining scalable ETL (Extract, Transform, Load) or ELT pipelines, often utilizing cloud platforms like AWS, Google Cloud, or Azure Cloud. They write complex code to ingest, clean, transform, and aggregate data from diverse sources into data lakes, warehouses, or lakehouses. Common responsibilities for these roles encompass collaborating with data analysts, scientists, and business stakeholders to understand data needs; optimizing database and query performance for large datasets; ensuring data security, quality, and governance standards are met; and documenting data models and pipeline architectures. They are also tasked with staying current with evolving technologies to modernize data stacks. Typical skills and requirements for Data Engineer/Data Modeler jobs include strong proficiency in SQL and one or more programming languages like Python, Scala, or Java. Expertise in data modeling tools (e.g., ERwin, ER/Studio) and hands-on experience with big data technologies (e.g., Apache Spark, Hadoop) and cloud data services (e.g., Azure Data Factory, AWS Glue, Google BigQuery) is highly valued. Knowledge of modern platforms like Databricks or Microsoft Fabric is increasingly common. Successful candidates usually possess a blend of deep analytical thinking, problem-solving abilities, and excellent communication skills to translate technical designs for non-technical audiences. A background in computer science, information systems, or a related field, along with several years of practical experience, is a standard expectation for these critical positions. Explore Data Engineer/Data Modeler jobs to find opportunities where you can architect the foundation of the information economy.
