Senior Data Engineer Con II
About The Position
Allstate's Data & Analytics Technology organization is seeking a Senior Data Engineer to lead the design, development, and operation of scalable, resilient, and high-performing data platforms that power enterprise analytics, reporting, AI, and advanced data products. This role will be responsible for building and optimizing batch and streaming data solutions using Apache Spark, Microsoft Fabric, and modern cloud-native data technologies. As a Senior Data Engineer, you will play a critical role in designing and implementing Lakehouse architecture based on Medallion (Bronze, Silver, Gold) principles, ensuring that data is governed, trusted, and readily consumable across the enterprise. You will partner closely with analytics engineers, architects, data scientists, and product teams to transform complex data into business-ready information assets through effective data engineering, modeling, and governance practices. The ideal candidate possesses deep experience in large-scale data processing, dimensional data modeling, and modern software engineering practices, including leveraging Agentic AI-assisted development tools to accelerate engineering productivity, code quality, testing, and operational excellence.
Requirements
- 7+ years of experience as a Data Engineer, building and supporting enterprise-scale data platforms and pipelines.
- Strong hands-on experience with Apache Spark for large-scale distributed data processing.
- Advanced proficiency in Python, SQL, and modern data engineering development practices.
- Experience designing and implementing Lakehouse architectures and Medallion Architecture patterns.
- Strong understanding of dimensional modeling, including star schemas, snowflake schemas, fact tables, dimension tables, and semantic data models.
- Experience with Microsoft Fabric, OneLake, Delta/Parquet formats, or comparable cloud analytics platforms.
- Expertise in data integration, transformation, and optimization techniques for large-scale analytical workloads.
- Experience implementing CI/CD pipelines, source control, Infrastructure as Code (IaC), and automated testing practices.
- Familiarity with AI-powered engineering tools and Agentic AI-assisted software development practices.
- Strong understanding of distributed computing, performance tuning, scalability, fault tolerance, and data reliability.
- Excellent problem-solving, collaboration, communication, and stakeholder management skills.
- Experience leading technical initiatives and mentoring engineering team members.
Nice To Haves
- Experience with real-time and event-driven architectures using Kafka, Event Streams, or similar technologies.
- Experience designing and implementing enterprise data products within Microsoft Fabric.
- Familiarity with Data Vault, Kimball, or other enterprise data modeling methodologies.
- Experience supporting machine learning, generative AI, or advanced analytics workloads.
- Knowledge of data governance, metadata management, master data management, and data quality frameworks.
- Cloud certifications or data engineering certifications in Azure, Microsoft Fabric, Databricks, or related technologies.
Responsibilities
- Design, build, and maintain scalable batch and real-time data pipelines using Apache Spark, SQL, Python, and cloud-native data technologies.
- Architect and implement Medallion Lakehouse data patterns (Bronze, Silver, Gold) to support enterprise analytics, reporting, operational intelligence, and AI use cases.
- Develop and optimize ETL/ELT pipelines that ingest, transform, standardize, and curate data from diverse enterprise source systems.
- Design and implement robust Dimensional models, including fact and dimension tables, to support reporting, self-service analytics, and business intelligence solutions.
- Build and manage data workloads within Microsoft Fabric, OneLake, and modern Lakehouse platforms.
- Establish data quality, observability, and governance frameworks through automated validation, reconciliation, monitoring, and alerting capabilities.
- Optimize Spark workloads for performance, scalability, reliability, and cost efficiency across large and complex datasets.
- Design solutions that effectively manage schema evolution, data lineage, metadata, and source-system changes.
- Develop reusable frameworks, accelerators, and engineering standards that improve consistency and delivery velocity across teams.
- Leverage Agentic AI-assisted development capabilities to accelerate code generation, testing, documentation, troubleshooting, and engineering productivity while adhering to enterprise governance standards.
- Implement CI/CD pipelines and DevSecOps practices to enable automated testing, deployment, release management, and rollback strategies.
- Lead technical design reviews and provide mentorship to junior and mid-level data engineers.
- Collaborate with data architects, business stakeholders, analytics engineers, and data scientists to translate business requirements into scalable data solutions.
- Partner with platform, security, privacy, and governance teams to ensure compliance with enterprise policies and regulatory requirements.
- Drive continuous improvement across data engineering practices, architecture standards, and Agile delivery processes.
Benefits
- Comprehensive technology setup, including a laptop, monitors, headset, keyboard, and mouse.
- Monthly connectivity reimbursement for eligible remote employees.
- Opportunity to shape the future of protection.
- Support for causes that mean the most to you.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
