Data Engineer

3 days ago

Arlington, Virginia, United States WTW inc. Full time
Job Summary

We are seeking a highly skilled Data Engineer to join our team at WTW inc. As a Senior Data Engineer, you will play a pivotal role in designing, building, and optimizing complex data pipelines and ETL processes while leveraging your expertise in Azure Synapse and PySpark to build advanced data analytics systems and data outputs for downstream consumption.

Key Responsibilities
  • ETL Development & Maintenance: Lead the design, development, and optimization of complex ETL processes and pipelines that enable reliable data ingestion, transformation, and loading across a variety of sources.
  • Azure Synapse Analytics: Architect and develop scalable data solutions utilizing Azure Synapse with a deep focus on performance optimization.
  • Data Modeling: Design and maintain logical and physical data models, ensuring data structures align with business needs, scalability, and optimization for data warehousing and analytics.
  • GenAI Integration for Data: Lead the application of Generative AI technologies for data analysis and data extraction, including leveraging GenAI for predictive analytics, automated data transformation, and natural language query processing.
  • Data Pipeline Automation: Develop, implement, and manage automated data pipelines for continuous integration and deployment of data solutions.
  • Collaboration: Work closely with other Data Engineers, analysts, and business stakeholders to understand their data requirements and provide innovative, scalable data solutions.
  • Performance Tuning & Optimization: Continuously monitor, evaluate, and optimize data pipelines and queries to enhance the performance of data systems, minimize latency, and ensure real-time data availability.
  • Cloud Engineering: Drive cloud-native engineering best practices on the Azure platform including security, scalability, high availability, disaster recovery, and cost-efficiency in data storage and processing.
  • Documentation & Best Practices: Create and maintain clear, concise documentation for data pipelines, models, and processes. Promote best practices for data governance, quality, and security.
Qualifications
  • 10+ years of experience in Data Engineering and ETL processes with proven expertise in building and optimizing data pipelines.
  • 5+ years of experience in Azure Synapse Analytics, including hands-on work with PySpark notebooks.
  • 5+ years of experience in Data Modeling, with strong expertise in relational and dimensional modeling that have been proven as successful deliverables of desired end usage as it relates to consumer report outcomes.
  • 3+ years of experience in integrating and leveraging Generative AI technologies for data-related functions such as data analysis, extraction, and transformation.
  • Expertise in Azure Cloud Ecosystem, including Azure Data Lake, Azure Data Factory, and Synapse Studio.
  • Strong proficiency in PySpark and distributed computing frameworks as it relates to the ingestion, transformation and distribution of data into established data frameworks or processes.
  • Solid understanding of Data Governance principles, including data security, data quality, and master data management (MDM), and understanding or usage of Purview for cataloging the lineage of the data assets for consumption.
  • Strong SQL skills and proficiency in other languages like Python, Scala, or R.
  • Experience with DevOps practices such as CI/CD pipelines for data workflows, including tools like Azure Git.
  • Familiarity with AI/ML workflows and the intersection of AI with data engineering processes.
Preferred Qualifications
  • Azure Certifications: Microsoft Certified: Azure Data Engineer Associate or Azure Solutions Architect Expert.
  • Experience with real-time data streaming solutions such as Azure Event Hubs.
  • Hands-on experience with BI Tools such as Power BI or similar.
  • Knowledge of NoSQL databases such as Cosmos DB or MongoDB.
  • Experience with containerization (Docker, Kubernetes) for data engineering workloads.
  • Experience in the Property and Casualty Insurance industry to allow accelerated learning of business structure and data needs.
Soft Skills
  • Problem-Solving Orientation: Ability to think critically and solve complex problems with innovative and practical solutions.
  • Strong Communication Skills: Capable of communicating effectively with both technical and non-technical stakeholders.
  • Leadership & Mentorship: Experience in mentoring junior engineers and leading technical initiatives across teams.
  • Adaptability & Continuous Learning: Openness to continuously learning new tools and technologies in the evolving landscape of cloud data engineering and AI.

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