Senior Data Engineer: Lakehouse, AI & Spark (Remote)

Allstate; United States; Remote; USD 91,000 - 154,000; Full time

Job summary

Allstate is seeking a Senior Data Engineer to design, build and operate scalable data platforms powering analytics, AI, and enterprise data products. You will develop batch and streaming pipelines, implement Medallion Lakehouse patterns, and drive governance and quality across ecosystems.

You will collaborate with analytics, data scientists, and product teams to deliver trusted data assets while leveraging AI-assisted development, CI/CD, and DevSecOps practices to ensure reliability and security.

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.

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.

Skills

Apache Spark

Python

SQL

Lakehouse

Medallion Architecture

Data Modeling

Data Governance

Tools

Microsoft Fabric

OneLake

Delta/Parquet

Job description

Allstate is seeking a Senior Data Engineer to design, build and operate scalable data platforms powering analytics, AI, and enterprise data products. You will develop batch and streaming pipelines, implement Medallion Lakehouse patterns, and drive governance and quality across ecosystems.

You will collaborate with analytics, data scientists, and product teams to deliver trusted data assets while leveraging AI-assisted development, CI/CD, and DevSecOps practices to ensure reliability and security.