Senior Software Engineer I

Primary Responsibilities:

Design, implement and maintain highly scalable microservices within all phases of the Software Development Life Cycle (SDLC) on Microsoft Azure and AWS
Establish, refine and integrate CI/ CD, feature testing automation, performance testing as needed
Design, build, and optimize data ingestion and transformation pipelines on Azure Databricks (Spark/Delta Lake) with attention to performance, cost, and operational simplicity
Implement streaming and batch data patterns; leverage CDC approaches and eventing/orchestration using services such as messaging/eventing and workflow orchestration tools (as applicable in the ecosystem)
Engineer high-quality PySpark code and SQL; enforce coding standards and enable reuse through shared libraries and notebooks
Build/extend APIs and microservices that expose curated data for real-time and near-real-time use cases; ensure backward compatibility and strong observability
Contribute to CI/CD practices (pipelines and deployment automation), infrastructure-as-code exposure, secrets management, and automated testing for data and services
Partner with architects/SMEs to migrate workloads to cloud-native paradigms with clear SLAs and operational expectations
Instrument solutions with end-to-end monitoring and alerting; drive MTTR reduction through runbooks, dashboards, and actionable alerts
Collaborate with business and platform teams to onboard new consumers, define data contracts, and scale solutions for growth in volume and usage
Contribute to data governance (catalog, lineage, access controls) and ensure compliance with enterprise standards
Participate in on-call/production support rotations; perform root-cause analysis and preventive hardening to maintain reliability and SLA adherence
Document designs, patterns, and operational guides to enable consistent delivery and rapid onboarding across teams
Required Qualifications:

Graduate or Postgraduate in Computer Science /Engineering/Science/Mathematics or related field with 6+ years of solid software engineering experience
Hands-on experience with Azure Databricks (Spark, Delta Lake) and strong PySpark + SQL skills for large-scale data processing
Experience building event-driven and batch data flows (orchestration + compute) and integrating with cloud services for reliable delivery
Experience with developing applications using Azure Kubernetes service (AKS), Service Fabric, App Service, function, azure Storage, service bus queues, event hubs, application gateway etc.
Experience to design and implement highly scalable Restful microservices
Experience in automating all deployment steps with Infrastructure as Code (IAC), CI/ CD pipelines using Git Action, terraform, MLOps etc.
Experience to define guidelines and benchmarks for NFR considerations during project implementation
API engineering experience (REST and/or gRPC), contract versioning, and service observability (logs, metrics, traces)
Good understanding of Infrastructure as code
Familiarity with secrets/access management practices and role-based access controls in data platforms
CI/CD exposure (Azure pipelines or equivalent), including test automation and deployment strategies for data and services
Proficient in AI tools like copilot, AI automation and productivity, knowledge in LLM, agentic AI framework
Proven production mindset: on-call readiness, incident response, and proactive post-incident improvements to protect SLAs for a large consumer base
Proven ability to design for reliability: idempotency, retries, transactional writes, schema evolution, and operating at scale (performance and cost tuning)
Proven quick learner and highly motivated team player and a self-starter. Should be able to learn new technology and able to deliver the work in short duration
Proven excellent verbal and written communication skills. Solid collaborator within and across multiple teams
Demonstrated ability to do required POCs to make sure that suggested design/technologies meet the requirements
Demonstrated ability to envision the overall solution for defined functional and non-functional requirements; and be able to define technologies, patterns and frameworks to materialize it
Demonstrated ability to design and develop the framework of the system and be able to explain choices made. Also write and review design document explaining overall architecture, framework and high-level design of the application
Demonstrated ability to create, understand and validate Design and estimated effort for given module/task, and be able to justify it
Demonstrated ability to define in-scope, out-of-scope and taken assumptions while creating effort estimates
Demonstrated ability to identify and integrate well over all integration points in context of a project as well as other applications in the environment
AI Expectations:
Proficient in AI tools like copilot, AI automation and productivity, knowledge in LLM, agentic AI framework
Proven ability to utilize AI tools and applications to perform tasks, make decisions and enhance their work and overall cloud migration velocity. Expected to use AI Powered software for data analysis, coding, and overall productivity

Preferred Qualifications:

Experience with Azure DevOps governance and enterprise controls
Experience migrating data/workloads to cloud; healthcare/claims domain exposure
Health care industry experience
Fundamental core ML and DL knowledge, NLP
Knowledge of Delta Live Tables, Structured Streaming, or Kafka-based ingestion patterns
Knowledge of Data quality frameworks (expectations, unit tests) and lineage/catalog tooling
Knowledge of performance engineering for Spark (partitioning, caching, join optimization, skew mitigation) and SQL warehouse optimization
Knowledge of Secure engineering practices (PHI/PII handling, encryption in transit/at rest, network isolation)