Snowflake Data Engineer - Ads

Apple; Hyderabad; On-site; INR 3,500,000 - 5,500,000; Full time

Job summary

Apple Ads, India, in Hyderabad, seeks a Cloud database warehouse engineer (Snowflake) to join the Reliability Engineering Team. You will build, operate, and automate the Snowflake data platform.

The role emphasizes Snowflake on AWS, Iceberg lakehouse, CI/CD, IaC (Terraform), ArgoCD, and Kubernetes, with Python scripting for automation.

Qualifications

• 5-8 years of ICT3 level experience in IT/software/database/data engineering.

Responsibilities

• Administer Snowflake accounts and warehouse resources.

Skills

Python programming

SQL

Automation scripting

Analytical thinking

Communication skills

Linux/Unix basics

AWS cloud concepts

Tools

Snowflake

Snowpark Python

Terraform

ArgoCD

ACK

Jenkins/GitHub Actions

CloudFormation

Iceberg

Kubernetes

Job description

Summary

At Apple, everything begins with the customer experience. Apple Ads extends this philosophy to advertising—helping people discover what they need while empowering advertisers to grow their businesses. Our technology delivers ads and sponsorships across Apple Services, including the App Store, Apple News, and MLS (Major League Soccer) Season Pass. Every solution we build is rooted in trust, connection, and impact: respecting user privacy, integrating advertising seamlessly into the Apple experience, and delivering value for advertisers of every size—from small app developers to global brands. When advertising is done right, it benefits everyone. Apple Ads, India is seeking Cloud database warehouse engineer (Snowflake) to join the Reliability Engineering Team in Hyderabad, to build, operate, and automate our database platform.

Description

In this role, you’ll work primarily with Snowflake on AWS, alongside open-format lakehouse technologies like Apache Iceberg, and grow your skills in cloud infrastructure, CI/CD, and automation. This is a hands‑on role for someone who enjoys scripting away toil, learning cloud-native tooling, and partnering with engineering teams. You’ll be mentored by senior engineers while taking increasing ownership of real production data platforms.

Key Responsibilities

• Administer Snowflake accounts, virtual warehouses, and database objects — including configuring auto-scaling, monitoring warehouse utilization, and implementing backup and recovery strategies via Time Travel and Cloning.
• Manage the full lifecycle of database objects, roles, and account‑level parameters across development, staging, and production environments.
• Proactively monitor and tune workloads using Snowsight Performance Explorer and the Account Usage schemas.
• Optimize query execution by analyzing query profiles for disk spillage, identifying Cartesian joins, and implementing Search Optimization Service or Query Acceleration Service where necessary.
• Implement autonomous warehouse management by right‑sizing clusters based on real‑time usage patterns, and configure auto‑suspend/resume with 60‑second timeouts to eliminate idle compute waste.
• Hands‑on experience with Apache Iceberg is a must — design and manage unified, open‑format storage that can be queried directly in Snowflake with warehouse‑level performance.
• Manage Snowflake Iceberg Tables in the AWS cloud.
• Develop advanced scripts and pipelines using Snowpark Python and Snowpark pandas APIs for scalable data transformations.
• Strong Python programming skills are a must — building automation tooling, reusable libraries, and operational scripts for administrative and data‑engineering tasks.
• Automate administrative tasks such as environment provisioning and security policy enforcement through Infrastructure as Code using Terraform.
• Build and manage declarative, GitOps‑driven infrastructure using ArgoCD, Custom Resource Definitions (CRDs), and AWS Controllers for Kubernetes (ACK) to provision and manage cloud resources natively from Kubernetes.
• Knowledge of CI/CD platforms and DevOps practices is a must — designing, building, and maintaining automated build, test, and deployment pipelines (e.g., Jenkins, GitLab CI, GitHub Actions), applying version control, automated testing, release management, and configuration management.
• Working knowledge of Amazon EKS and core AWS components — including S3, API/NAT Gateway, PrivateLink, Load Balancing (ELB/ALB/NLB), and VPC peering.
• Configure and support secure data sharing between Snowflake accounts and across cloud boundaries.
• Implement Role‑Based Access Control (RBAC), Multi‑Factor Authentication (MFA), and data masking policies, ensuring compliance with data protection standards such as GDPR and HIPAA.

Minimum Qualifications

• 5-8 years of experience for ICT3 in IT / software / database / data engineering
• Hands‑on experience with Snowflake (or another cloud data warehouse such as Redshift, BigQuery, or Databricks with a clear path to Snowflake); solid understanding of core database and data‑warehousing concepts.
• Strong working knowledge of SQL and fundamentals of query tuning and execution plans (e.g., reading query profiles, clustering/pruning basics, warehouse sizing, and Account Usage / Information Schema views).
• Experience with at least one major cloud provider, ideally AWS, and its core services (compute, managed storage such as S3, networking, security groups, VPC, PrivateLink, KMS, IAM).
• Proficiency in Python for scripting and automation (Snowpark Python a plus).
• Exposure to CI/CD and DevOps tooling (e.g., Git, Jenkins/GitLab CI/GitHub Actions, and infrastructure‑as‑code such as Terraform or CloudFormation, Helm, ArgoCD, ACK).
• Exposure to open‑format lakehouse technologies such as Apache Iceberg (a plus).
• A demonstrated bias toward automation — a track record of scripting or automating repetitive work.
• Working knowledge of Linux/Unix fundamentals.
• Strong analytical and problem‑solving skills, eagerness to learn, and good communication and collaboration skills.

Preferred Qualifications

• Exposure to GenAI application building, MCP and prompt engineering
• Experience with Streamlit, Snowpark Python / Snowpark pandas for in‑database transformations.
• Hands‑on with Apache Iceberg tables, external volumes, and open‑format lakehouse patterns.
• Familiarity with AWS networking and connectivity — PrivateLink, VPC peering, gateways, and load balancing.
• Experience building and orchestrating data pipelines (e.g., dbt, Airflow, Dagster, or Snowflake Tasks & Streams).
• Exposure to observability and monitoring stacks (e.g., Datadog, Prometheus/Grafana, Splunk).