A Data Analyst develops computer programs to analyze large customer information databases for companies and organizations. Analyzes data to identify patterns and provide information relevant to a particular business, industry or field; analysis may be used for marketing, or to detect fraud in financial transactions, or for research. Develops computer programs to protect confidential customer information.
Sr. Analyst, Data Engineering Data Engineering is the practice of designing, building, and maintaining systems that collect, store, process, govern, and analyze large volumes of data required by analysts, data scientists, and AI/ML applications. It serves as the foundation for enabling data-driven insights, intelligent automation, and AI-powered decision-making across the organization. What You'll Achieve Data Management, Engineering & AI Enablement - ETL/ELT processing, data transformation, data quality, governance, security, and AI-ready data architectures across enterprise platforms. You will help enable trusted, high-quality data to support analytics, reporting, machine learning, agentic AI solutions, and operational decision-making.
Essential Requirements:
Databases & SQL :
Proficiency in Teradata, PostgreSQL, and SQL for querying, transforming, profiling, and validating data, with a strong understanding of relational, dimensional, and analytical data models to accurately map source-to-target schemas
ETL/ELT & Development Practices :
Experience with Informatica, Apache Airflow, or comparable data integration platforms, along with familiarity with version control and CI/CD practices using Git-based development workflows
Programming & Automation :
Proficiency in Python for automation, orchestration, and custom data solutions, with the ability to manage unexpected data quality issues, platform constraints, and migration challenges with agility
Data Quality & Governance :
Understanding of data lineage, metadata management, master data management (MDM), and governance concepts to ensure data integrity and compliance throughout the data lifecycle
AI-Ready Data Engineering :
Knowledge of data preparation, feature engineering concepts, and dataset management for AI/ML workloads, enabling trusted and well-governed datasets for advanced analytics and model development
Desirable Requirements:
Analytics, Reporting & Modern Data Platforms :
Knowledge of Power BI or other visualization tools, experience with Airflow, enterprise schedulers, SSIS, SSRS, and Tabular OLAP/semantic modeling, along with understanding of MLOps, DataOps, cloud-native data services, modern lakehouse architectures, and data observability/automated anomaly detection solutions
AI & Intelligent Automation :
Exposure to machine learning and AI technologies for automation and operational efficiency, including familiarity with Agentic AI concepts (LLMs, prompt engineering, vector databases, semantic search, responsible AI/AI governance), experience using AI-powered productivity tools such as GitHub and Devin, and understanding of modern AI-driven development practices Compensation Dell is committed to fair and equitable compensation practices. The salary range for this position is $102,000 - $132,000. Benefits and Perks of working at Dell Technologies Your life. Your health. Supported by your benefits. You can explore the overall benefits experience that awaits you as a Dell Technologies team member — right now at MyWellatDell.com
You Will:
Data Migration & Mapping :
Analyze source and target database structures, identify data dependencies, constraints, and transformation needs, and create source-to-target mapping documents with defined transformation rules and business logic in collaboration with stakeholders
Data Pipeline Design & Architecture :
Work with structured and unstructured data to design and implement scalable data pipelines that support analytics, AI, and machine learning workloads, aligning schemas, relationships, and data models with data architects
Data Quality & Governance :
Develop processes to improve data quality, observability, lineage, and governance while ensuring data platforms comply with enterprise security, privacy, and responsible AI standards
AI/ML Enablement & Agentic AI Support :
Partner with data scientists, AI engineers, and business teams to enable trusted datasets for AI/ML model development and support implementation of data solutions for Agentic AI use cases
AI-Driven Development & Automation :
Leverage AI-assisted development tools to improve productivity and documentation quality, and evaluate opportunities for intelligent automation using AI and machine learning techniques within data engineering processes
