At Allstate, great things happen when our people work together to protect families and their belongings from life's uncertainties. And for more than 90 years, our innovative drive has kept us a step ahead of our customers' evolving needs. From advocating for seat belts, air bags and graduated driving laws, to being an industry leader in pricing sophistication, telematics, and, more recently, device and identity protection.
Job DescriptionJob Description
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.
- 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.
Required Qualifications - 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.
Preferred Qualifications - 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.
Leadership Expectations - Serve as a technical leader and trusted advisor for data engineering best practices.
- Champion modern data architecture patterns, reusable engineering standards, and automation-first approaches.
- Drive adoption of AI-assisted engineering practices to improve developer productivity and solution quality.
- Mentor and coach engineering team members while fostering a culture of innovation, ownership, and continuous learning.
Supervisory Responsibilities This position does not have direct people management responsibilities but is expected to provide technical leadership, mentorship, and guidance across project teams and engineering initiatives.
SkillsApache Spark, Data Engineering, Python (Programming Language), Structured Query Language (SQL)
CompensationCompensation offered for this role is 90,700.00 - 153,925.00 annually and is based on experience and qualifications.
The candidate(s) offered this position will be required to submit to a background investigation.
Joining our team isn't just a job - it's an opportunity. One that takes your skills and pushes them to the next level. One that encourages you to challenge the status quo. One where you can shape the future of protection while supporting causes that mean the most to you. Joining our team means being part of something bigger - a winning team making a meaningful impact.Allstate generally does not sponsor individuals for employment-based visas for this position.
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Allstate provides a comprehensive technology setup, including a laptop, monitors, headset, keyboard, and mouse. Employees eligible to work from home also receive a monthly connectivity reimbursement to help offset internet costs.
When working from home, you must have a dedicated, private workspace free from distractions, along with appropriate desk and seating. Reliable internet is required, with minimum speeds of 50 MB download and 5 MB upload.
To help create a more personal and engaging experience, we ask that you join with your camera on if your interview is virtual. Please also have a Valid Photo Identification available at the start of your interview. If you require any accommodations or have questions ahead of your interview please reach out to your recruiter.
Note: Internal candidates are only asked to join on video; a Valid Photo Identification is not needed for the interview.
