Data & ML Ops Engineer

Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other's ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It's the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you'll do more than join something - you'll add something.

Apple Pay brought mobile payment to millions of customers, and it's just the beginning. We are looking for engineers who enjoy both hands-on technical work and designing thoughtful, scalable services for Wallet and Apple Pay. Our team's vision is to be the engine of intelligent transformation, leveraging a unified, reliable data platform to build and deploy innovative and solutions that drive significant business impact and enable data-driven decision-making throughout the organization.

Description

As data operations engineer, you will be a key member of Wallet Payments & Commerce Data Foundations team, and will collaborate with various infrastructure teams, platforms, product, engineering and scientists, to identify requirements that will derive the creation of sensible operations to build and manage data, AML Foundations. The ideal candidate is a self-motivated teammate with good problem solving and communication skills with the ability to adapt and learn quickly, provide results with limited direction, and choose the best possible operational decisions.","responsibilities":"Lead day-to-day operations to ensure organizational delivery of quality results.

You will be responsible for provisioning, enabling, scaling and maintaining our team's data and ML infrastructures for batch and real time systems including pipelines, frameworks, tools and services in hybrid cloud.

Shepherd zero-downtime deployment process through continuous delivery practices, rapidly releasing features that provide critical and faster insights to business users.

Collaborate with the platform team by building the right tools for observability, monitoring, alerting and self-healing for the day to day management of analytics foundations.

Enable ML platform for larger adoption across wallet product teams to bring advanced analytics capabilities.

Manage, deploy and scale various tiered data stores for supporting various data products.

Debug complex problems in distributed environment and ability to run the prod incidents efficiently to following up with Post incident reviews.

Developing self-service tools and automation to improve engineering efficiency and the quality of services.

Preferred Qualifications

Excellent verbal and written communication skills

Self-starter with forward thinking capability with strong executional track record and be accountable for business priorities

Hands-on experience with CI/CD pipelines and cloud environments like Gitlab, Spinnaker , Docker/Kubernetes observability using Splunk, Grafana/Data dog is a huge plus.

Experience scaling operations in a fast-paced and dynamic environment. Experience working in agile or evolving product environments

Minimum Qualifications

5+ years of professional experience with large scale platform operations and support

3+ years of experience in developing and operating infrastructure, data platforms and cloud services in public cloud environments (AWS or GCP)

Proficient with one or more programming languages such as Python/Go/Rust/Java/Scala for automation and API integrations

Experience in implementing security controls, governance processes, compliance validation, infrastructure cost analysis and optimization

Hands on experience in network protocol, load balancing technologies and transport protocols and Linux/Unix system internals and scripting.

Knowledge of analytics and Applied ML stack like Apache Spark, Trino/Pinot, Iceberg, Atlas, Flink, Airflow/Luigi, MLFlow, Data Catalogs, Jupyter Notebooks, Vector database and Cassandra

Solid understanding of IAAC (infrastructure as a code) techniques like terraform, orchestration and tooling.","internalDetails":null,"eeoContent":null