Data & AI Engineer

Our Company
Explore how you can contribute at AmeriLife.
For over 50 years, AmeriLife has been a leader in the development, marketing and distribution of annuity, life and health insurance solutions for those planning for and living in retirement.
Associates get satisfaction from knowing they provide agents, marketers and carrier partners the support needed to succeed in a rapidly evolving industry.
Job Summary
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.
The role will focus on advancing AmeriLife's Databricks Lakehouse platform, supporting the transition from legacy data environments to scalable, cloud-native architectures, and enabling enterprise analytics, reporting, and automation.
This is a hands-on engineering position requiring exceptional proficiency in Python, PySpark, and SQL, a strong foundation in modern data engineering, and a hyperautomation mindset focused on eliminating manual processes, building reusable engineering frameworks, and improving operational efficiency.
The ideal candidate is a technically curious, execution-oriented engineer who enjoys writing code, solving complex problems, and applying modern technologies to build scalable, automated enterprise data solutions.
Job Description
Key Responsibilities
Enterprise Data Engineering & Databricks
Design, develop, test, 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 (Bronze, Silver, and Gold) principles.
Develop reusable, modular, and metadata-driven ETL/ELT frameworks to support enterprise data processing.
Ingest and integrate structured and semi-structured data from databases, APIs, flat files, cloud storage, and enterprise applications.
Work with Delta Lake, Unity Catalog, Databricks Workflows, and related platform capabilities.
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 and engineering challenges.
Collaborate with senior engineers and architects to implement enterprise data engineering standards and best practices.
Hyperautomation & Intelligent Engineering
Apply a hyperautomation-first mindset to identify opportunities for eliminating repetitive manual processes across data engineering and platform operations.
Develop Python-based automation frameworks, utilities, and reusable engineering components.
Design and implement metadata-driven and configuration-driven solutions that reduce manual coding and operational dependencies.
Leverage scripting, APIs, orchestration, and intelligent automation techniques to streamline engineering workflows.
Explore practical applications of AI-assisted development and intelligent automation to accelerate engineering productivity.
Continuously identify opportunities to simplify processes, reduce technical debt, and improve engineering efficiency through automation.
Data Integration & Transformation
Develop efficient data transformation logic using SQL, Python, and PySpark.
Implement joins, aggregations, business rules, enrichment logic, and incremental processing patterns.
Work with relational databases, APIs, JSON, Parquet, Delta, and other common enterprise data formats.
Support dimensional modeling, historical data tracking, and efficient data processing patterns.
Collaborate with business analysts, data architects, and technical teams to translate requirements into engineering solutions.
Build scalable and reusable integration components following enterprise engineering standards.
Data Quality, Governance & Reliability
Implement automated data quality checks covering completeness, accuracy, consistency, uniqueness, and referential integrity.
Develop reconciliation and validation routines across source systems, transformation layers, and downstream datasets.
Support enterprise metadata management, data lineage, and cataloging initiatives.
Apply data governance standards using Unity Catalog and other enterprise governance capabilities.
Build monitoring, logging, and alerting capabilities for data pipelines and scheduled workloads.
Troubleshoot pipeline failures, data discrepancies, performance issues, and integration errors.
Participate in root-cause analysis and implementation of permanent corrective solutions.
Follow established security, access control, data privacy, and auditability requirements.
Engineering Automation & DevOps
Develop and maintain engineering code using Git-based version control and collaborative development practices.
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.
Support configuration-driven and metadata-driven pipeline development.
Contribute to infrastructure automation and environment management practices.
Document technical designs, data mappings, transformations, deployment procedures, and operational runbooks.
Continuously identify oppor