Data Platform Engineer - Airflow Platform Specialist at Apple

Hyderabad, Telangana, India -

Full Time

Start Date

Immediate

Expiry Date

17 May, 26

Salary

0.0

Posted On

16 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Airflow, Platform Engineering, Python, Kubernetes, Docker, AWS EMR, Spark, Hadoop, Terraform, CI/CD, Prometheus, Grafana, API Design, Distributed Systems, PostgreSQL, Java

Industry

Computers and Electronics Manufacturing

Description

At Apple, we work every single day to craft products that enrich people’s lives. Our Advertising Platforms group makes it possible for people around the world to easily access informative and imaginative content on their devices while helping publishers and developers promote and monetize their work. Our technology and services power advertising in Apple News and Search Ads in App Store. Our platforms are highly-performant, deployed at scale, and setting new standards for enabling effective advertising while protecting user privacy. As part of our geographical expansion, we’re looking for strong Software Development Engineer (Data) to build highly scalable data platforms and services. The people here at Apple don’t just build products — they build the kind of wonder that’s revolutionized entire industries. It’s the diversity of those people and their ideas that inspires the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts. Join Apple, and help us leave the world better than we found it. Imagine what you could do here. DESCRIPTION We're seeking an exceptional Data Platform Engineer with deep expertise in Apache Airflow to build, scale, and maintain our data orchestration platform. This is a platform engineering role - you'll be building the infrastructure and tooling that enables other data engineers to orchestrate their workflows, not building data pipelines yourself. MINIMUM QUALIFICATIONS Deep Airflow Expertise 5+ years in Platform/Infrastructure Engineering or Data Platform Engineering 3+ years of deep, hands-on experience with Apache Airflow internals: Understanding of Airflow architecture components (scheduler, executor, webserver, metadata DB) Experience customizing and extending Airflow core (not just using it) Knowledge of executor implementations Understanding of Airflow's DAG parsing, scheduling, and execution model Experience with Airflow plugin development and custom operators Ability to read and modify Airflow source code Infrastructure & Platform Skills Expert-level Python (advanced programming, not just scripting) Strong Java proficiency for Spark/Hadoop integrations Production experience with Kubernetes (deployments, operators, Helm) Deep understanding of containerization (Docker, multi-stage builds) Experience with AWS EMR cluster management and APIs Knowledge of Hadoop ecosystem architecture (HDFS, YARN, resource managers) Experience with Apache Spark architecture and cluster modes Platform Engineering Distributed systems concepts and design patterns Database performance tuning (PostgreSQL/MySQL for Airflow metadata) Message queuing systems Infrastructure as Code (Terraform, CloudFormation, Pulumi) CI/CD systems (Jenkins, GitLab CI, GitHub Actions) Monitoring and observability (Prometheus, Grafana, ELK, Datadog) Software Engineering Strong software design principles and architectural patterns Experience building frameworks, libraries, and developer tools Test-driven development and comprehensive testing strategies Version control and collaborative development practices API design and development (REST, gRPC) Performance profiling and optimization PREFERRED QUALIFICATIONS Active contributions to Apache Airflow open-source project Experience running Airflow at massive scale (1000+ DAGs, 100K+ daily tasks) Experience building multi-tenant data platforms Experience with GitOps and declarative infrastructure Background in SRE or platform reliability engineering Experience in digital advertising or high-scale data platforms

Responsibilities

The role focuses on building, scaling, and maintaining a highly scalable data orchestration platform centered around Apache Airflow infrastructure and tooling. This involves engineering the underlying platform that enables other data engineers to orchestrate their workflows, rather than building the data pipelines themselves.