Let's begin! Senior Software Engineer

Moody's•Newark, NJ

About The Position

Moody’s is seeking a Senior Software Engineer for its Data Platform Team, which builds and operates data services powering Moody’s Insurance Solutions products. The team owns a self-hosted Microsoft SQL Server estate running on Linux and Kubernetes in AWS, including its availability, performance, recoverability, and cost. It is also building the change-capture path that makes operational data available for analytics in a lakehouse. The role contributes to mission-critical infrastructure and helps shape the responsible adoption of modern data and AI technologies across the organization. The position operates and continuously improves the SQL Server platform, ensuring availability, performance, security, and data integrity. It includes administering SQL Server on Linux and Kubernetes, managing AWS storage and capacity, operating backup and high-availability systems, tuning databases, building change data capture pipelines, and developing platform services, automation, infrastructure as code, and CI/CD pipelines for database changes. The engineer participates in on-call rotation, leads incident response, improves observability, and proposes platform improvements through design reviews. The role requires strong production experience with backend or data platform systems, hands-on SQL Server administration, Kubernetes stateful workloads, AWS, and C#/.NET backend engineering. Candidates should demonstrate proficiency with AI concepts and tools, the ability to implement AI-powered solutions, and growing awareness of AI risk management and responsible use. A bachelor’s or master’s degree in Computer Science, Engineering, or a related discipline is sought, or equivalent professional experience. For US-based roles, the anticipated hiring base salary range is $139,800.00–$202,800.00, depending on experience, education, level, skills, and location. The role is eligible for incentive compensation. Moody’s offers a competitive benefits package.

Requirements

At least 5 years building and operating backend or data platform systems in production environments.
Strong hands-on SQL Server administration, including query and index tuning, execution plans, wait statistics, Query Store, and tempdb management; SQL Server on Linux experience or adjacent depth with a demonstrable fast ramp.
Experience with backup, restore, and high availability, including Always On Availability Groups, and recovery proven against recovery point objective and recovery time objective targets.
Experience with fleet operations, including configuration standards, drift detection, patching, and maintenance automation.
Knowledge of database security, including encryption at rest and in transit using Transparent Data Encryption, least-privilege access, auditing, and key and certificate rotation.
Knowledge of data integrity practices, including scheduled consistency checks, corruption detection, and recovery.
Experience with proactive monitoring of performance baselines, plan regressions, blocking, deadlocks, log growth, replica health, and job failures.
Production Kubernetes experience with stateful workloads, including StatefulSets, persistent volumes, and resource limits.
Production AWS experience across Elastic Kubernetes Service, Elastic Compute Cloud, Elastic Block Store, Simple Storage Service, Identity and Access Management, Virtual Private Cloud, and CloudWatch.
Strong backend engineering skills in C#/.NET, including REST APIs, microservices, testing, and code quality practices.
Linux fluency and clear written communication for a distributed team.
Demonstrated proficiency in artificial intelligence concepts and hands-on experience using AI tools to streamline workflows and enhance operational efficiency.
Proven ability to implement AI-powered solutions to solve business challenges.
Growing awareness of AI risk management and commitment to responsible and ethical AI use.
Bachelor’s or master’s degree in Computer Science, Engineering, or a related discipline, or equivalent professional experience.

Nice To Haves

Production-scale SQL Server replication and change data capture experience, including failure modes.
Backup to S3-compatible object storage and Elastic Block Store snapshot strategy experience.
Exposure to lakehouse technologies such as Apache Iceberg or Delta Lake.
Streaming and integration experience with Kafka, Debezium, AWS Database Migration Service, and Spark.
Infrastructure as code experience using Terraform and Helm.
Experience with very large database operations.
Working knowledge of Java.

Responsibilities

Operate and continuously improve a self-hosted SQL Server estate on Linux and Kubernetes, ensuring availability, performance, and data integrity for Moody’s Insurance Solutions products.
Operate and harden SQL Server on Linux on Amazon EKS, managing StatefulSets, storage classes, pod quality of service, upgrades, and day-two automation.
Own availability and recoverability by configuring and exercising Availability Groups, running failovers, and proving restores against recovery point objective and recovery time objective targets.
Tune performance at the query, index, and instance level, and maintain configuration baselines, drift detection, scheduled patching, and maintenance automation.
Keep databases secure and auditable through encryption, least-privilege access, auditing, and key and certificate rotation.
Protect data integrity through scheduled consistency checks, corruption detection, and tested recovery.
Baseline performance and alert on plan regressions, blocking, deadlocks, log growth, replica health, and job failures.
Build change data capture pipelines with attention to latency, ordering, schema evolution, and backfills.
Manage AWS storage and capacity, including Elastic Block Store volume types, IOPS and throughput headroom, expansion, snapshots, and cost.
Write platform services, automation, and infrastructure as code.
Maintain continuous integration and continuous delivery pipelines for database changes.
Participate in an on-call rotation, lead incident response, build observability that shortens future incidents, and propose platform improvements through design reviews.