[Hiring] Data & AI Engineer @AmeriLife

Role Description

AmeriLife is seeking a Data & AI Engineer to help build, modernize, and scale our enterprise data and artificial intelligence capabilities. Reporting to the Director of Data Engineering within the Enterprise Data & AI Platforms organization, this individual will play a hands-on engineering role in developing modern data pipelines, integrating enterprise data sources, and driving intelligent automation across the organization.

• Focus on advancing AmeriLife's Databricks Lakehouse platform.
• Support the transition from legacy data environments to scalable, cloud-native architectures.
• Enable enterprise analytics, reporting, and automation.
• Hands-on engineering position requiring proficiency in Python, PySpark, and SQL.
• Strong foundation in modern data engineering and a hyperautomation mindset.
• Enjoy writing code, solving complex problems, and applying modern technologies.

Qualifications

• Bachelor's degree in Computer Science, Information Technology, Engineering, Data Science, or a related technical discipline, or equivalent practical experience.
• 2+ years of hands-on experience in data engineering, software engineering, data integration, or related technical roles.
• Very strong Python programming skills, including scripting, modular development, data processing, and automation.
• Very strong SQL proficiency, including complex queries, joins, aggregations, window functions, transformations, and query performance optimization.
• Strong hands-on PySpark experience, including distributed data processing, DataFrame operations, transformations, and performance optimization.
• Experience developing ETL/ELT pipelines and working with relational databases or modern data platforms.
• Working knowledge of Databricks, Apache Spark, Delta Lake, or comparable cloud-based data engineering technologies.
• Understanding of modern data architecture concepts, including data lakes, data warehouses, lakehouses, and data pipelines.
• Experience working with structured data, JSON, CSV, Parquet, APIs, and other common integration formats.
• Familiarity with cloud platforms such as Microsoft Azure or AWS.
• Basic understanding of data modeling, data quality, and data validation practices.
• Experience using Git or comparable source control tools.
• Strong automation and hyperautomation mindset.
• Strong analytical, troubleshooting, and problem-solving skills.
• Ability to work independently on assigned engineering tasks while collaborating effectively within a broader technical team.
• Strong communication skills and willingness to learn new technologies.

Requirements

• Develop and maintain scalable data ingestion, transformation, and integration pipelines using Databricks, PySpark, Python, and SQL.
• Build and enhance enterprise data pipelines following modern Lakehouse and Medallion Architecture principles.
• Develop reusable, modular, and metadata-driven ETL/ELT frameworks.
• Ingest and integrate structured and semi-structured data from various sources.
• Support migration and modernization of legacy SQL Server, SSIS, and traditional data warehouse workloads onto Databricks.
• Optimize data pipelines and transformations for performance, reliability, scalability, and cost efficiency.
• Apply advanced Python, PySpark, and SQL techniques to solve complex data processing challenges.
• Collaborate with senior engineers and architects to implement enterprise data engineering standards.
• Apply a hyperautomation-first mindset to identify opportunities for eliminating repetitive manual processes.
• Develop Python-based automation frameworks, utilities, and reusable engineering components.
• Design and implement metadata-driven and configuration-driven solutions.
• Leverage scripting, APIs, orchestration, and intelligent automation techniques.
• Continuously identify opportunities to simplify processes and improve engineering efficiency through automation.
• Implement automated data quality checks covering completeness, accuracy, consistency, uniqueness, and referential integrity.
• Develop reconciliation and validation routines across source systems.
• Support enterprise metadata management, data lineage, and cataloging initiatives.
• Build monitoring, logging, and alerting capabilities for data pipelines.
• Troubleshoot pipeline failures, data discrepancies, performance issues, and integration errors.
• Follow established security, access control, data privacy, and auditability requirements.
• Develop and maintain engineering code using Git-based version control.
• Support automated deployment pipelines using CI/CD frameworks and DevOps tooling.
• Implement unit tests, integration tests, and automated validation for data engineering workloads.
• Create reusable Python utilities, notebooks, SQL scripts, and engineering components.
• Document technical designs, data mappings, transformations, deployment procedures, and operational runbooks.

Benefits

• A comprehensive benefits package that includes PTO, medical, dental, vision, retirement savings, disability insurance, and life insurance.