Software Engineer - Applications

Apple•Cupertino, CA

8h•$212,000 - $318,400•Onsite

About The Position

Design and develop software solutions, data models, and self-service analytics platforms by translating business requirements into scalable technical designs. Collaborate with cross-functional teams to support business analytics and improve data accessibility and usability. Build reporting, dashboards, and KPIs to track platform usage, cost, performance, and adoption, enabling data-driven business and executive decision-making. Capture, transform, and analyze system and platform data to generate actionable insights and operational metrics. Optimize database, ETL, and query performance through workload analysis, automation, and continuous system-wide tuning. Develop custom Platform frameworks, tools, and telemetry solutions for monitoring, performance analysis, and proactive issue detection. Implement workload management strategies to meet response-time SLAs while optimizing resource consumption. Lead evaluation, testing, proof-of-concepts, rollout, and adoption of new platform features in partnership with vendor and internal teams. Identify system gaps and deliver scalable, cloud-based solutions aligned with best practices. Partner with stakeholders on data governance, reporting standards, hierarchy management, and KPI definition. Ensure data security, privacy, and regulatory compliance (GDPR, CCPA, SOX) through RBAC, audits, data masking, and automated security controls. Support change management, documentation, and knowledge sharing to ensure smooth adoption of new tools, features, and processes.

Requirements

Bachelor’s degree or foreign equivalent in Electronic Engineering, Electrical Engineering, or a related field.
5 years of progressive, post-baccalaureate experience in the job offered or related occupation.
Designing, developing, and supporting cloud-based data platform using S3, Glue, Lambda, API Gateway, EKS, CloudWatch, and AWS.
Building highly scalable, fault tolerant data transformation pipeline using Driver Program, Cluster Manager, Executors, tasks, Spark SQL, MLlib, RDD, Partitions, DataFrame, Caching, and Spark.
Building and maintaining enterprise data warehouse and enabling business users to run their analytics efficiently using MPP architecture, Shared-Nothing architecture, AMP, T-SQL, Macros/procedure, Utilities, Viewpoint, Partitioning, Optimization techniques, Expansion, and Access control Teradata.
Designing, developing, and maintaining a highly secured, scalable, fault tolerant multi petabyte data platform using Cloud based architecture, SAAS based cloud data platform, Compute-Storage isolation, Virtual Warehouse, Database structures, Performance optimization, security, Scalability, Data Exchange, DR strategy, and Cost management.
Building automations, frameworks, and internal tools for efficiently and effectively running data platform using Shell scripting, CLI, file handling, text processing, networking, virtual memory, cron jobs, disk management, multitasking, logging and monitoring, and Unix.
Building data warehousing and analytical reporting solutions.
Designing, developing, and implementing ETL using Custom ETL, and BODS.
Building AWS enterprise data platform using S3, Glue, Lambda, Cloudwatch, API, Python, SQL, or Redshift.

Responsibilities

Design and develop software solutions, data models, and self-service analytics platforms.
Translate business requirements into scalable technical designs.
Collaborate with cross-functional teams to support business analytics and improve data accessibility and usability.
Build reporting, dashboards, and KPIs to track platform usage, cost, performance, and adoption.
Capture, transform, and analyze system and platform data to generate actionable insights and operational metrics.
Optimize database, ETL, and query performance.
Develop custom Platform frameworks, tools, and telemetry solutions.
Implement workload management strategies to meet response-time SLAs while optimizing resource consumption.
Lead evaluation, testing, proof-of-concepts, rollout, and adoption of new platform features.
Identify system gaps and deliver scalable, cloud-based solutions aligned with best practices.
Partner with stakeholders on data governance, reporting standards, hierarchy management, and KPI definition.
Ensure data security, privacy, and regulatory compliance (GDPR, CCPA, SOX).
Support change management, documentation, and knowledge sharing.