Ad-Tech (Advertising Technology)
- Programmatic Advertising: Programmatic advertising involves using automated systems and algorithms to buy and sell ad inventory in real-time. This process allows for efficient targeting, optimization, and delivery of ads to specific audiences.
- Ad Exchanges: Ad exchanges are platforms that facilitate the buying and selling of ad inventory. They connect publishers with advertisers, enabling the real-time auction of ad space to the highest bidder.
- Demand-Side Platforms (DSPs) and Supply-Side Platforms (SSPs): DSPs are used by advertisers to manage multiple ad exchange and data exchange accounts through a single interface, allowing for efficient buying of ad impressions. SSPs, on the other hand, help publishers manage and optimize their ad inventory.
- Data Management Platforms (DMPs): DMPs collect, analyze, and leverage large sets of data to help advertisers target specific audiences more effectively. They play a crucial role in audience segmentation and personalization.
- Ad Servers: Ad servers manage the delivery of ads to users' devices. They ensure that ads are displayed correctly, track impressions, and measure the effectiveness of ad campaigns.
- Ad Fraud Prevention: Ad-Tech involves various tools and techniques to prevent ad fraud, such as fake clicks and impressions, which can negatively impact the performance and ROI of advertising campaigns.
MarTech (Marketing Technology):
- Customer Relationship Management (CRM): CRM tools help businesses manage and analyze customer interactions throughout the customer lifecycle. They store customer data, track leads, and facilitate communication between sales, marketing, and customer service teams.
- Email Marketing Platforms: MarTech includes platforms for managing and automating email marketing campaigns. These tools enable businesses to send targeted and personalized messages to their audiences.
- Content Management Systems (CMS): CMS platforms allow marketers to create, manage, and optimize digital content for websites, blogs, and other online channels.
- Social Media Management: MarTech tools for social media management assist marketers in scheduling posts, analyzing engagement, and managing interactions across various social media platforms.
- Analytics and Reporting: Marketing analytics tools help marketers measure the performance of their campaigns, track key performance indicators (KPIs), and make data-driven decisions.
- Search Engine Optimization (SEO) Tools: MarTech includes tools to optimize websites for search engines, improve organic search rankings, and analyze the effectiveness of SEO strategies.
- Marketing Automation: Marketing automation platforms enable the automation of repetitive marketing tasks, such as email campaigns, lead nurturing, and customer segmentation.
Mandatory Skills:
- Apache Spark: Spark Core: An open-source, distributed computing system that provides an in-memory data processing engine for big data processing, making it faster than traditional batch processing systems like MapReduce. Spark SQL, Spark Streaming, MLlib, GraphX: Additional libraries and components built on top of Spark for SQL queries, real-time data processing, machine learning, and graph processing.
- Apache Kafka: A distributed event streaming platform that enables the ingestion and processing of real-time data streams. Kafka is often used for building data pipelines and supporting event-driven architectures.
- Apache Flink: A stream processing framework for big data processing and analytics. Flink is designed for low-latency, high-throughput, and exactly-once processing of data streams.
- NoSQL Databases: Cassandra, MongoDB, Couchbase: NoSQL databases that are suitable for handling unstructured or semi-structured data with horizontal scalability.
- Data Warehousing: Amazon Redshift, Google BigQuery, Snowflake: Cloud-based data warehouses that allow for high-performance querying and analysis of large datasets.
- ETL (Extract, Transform, Load) Tools: Apache NiFi, Talend, Informatica: ETL tools facilitate the extraction, transformation, and loading of data from various sources into data storage or analytical systems.
- Containerization and Orchestration: Docker, Kubernetes: Containerization tools to package and deploy applications, and orchestration tools to manage and scale containerized applications efficiently.
- Batch and Stream Processing: Apache Beam: A unified model for both batch and stream processing that can run on various distributed processing backends. Storm, Samza: Stream processing frameworks for real-time data analytics.
- Data Lakes: Amazon S3, Azure Data Lake Storage, Google Cloud Storage: Cloud-based storage solutions that allow organizations to store large volumes of raw, unstructured data for later analysis.
- Workflow Management: Apache Airflow, Luigi: Workflow management systems for orchestrating complex data processing tasks and dependencies.
- Machine Learning Integration: MLflow: An open-source platform to manage the end-to-end machine learning lifecycle, including experimentation, reproducibility, and deployment.
Other Responsibilities:
You will be responsible for designing and developing software products that provide measurement data to a wide set of users across all of Amazon's advertising suite and Freewheel solutions. You will be able to demonstrate a variety of architectural approaches and design patterns and have a demonstrated competence in designing maintainable and scalable software written in a high-level language We enable advertisers to optimize ad spend and allocate budgets effectively by providing accurate, actionable and timely conversion measurement our streaming ad products.
- 8+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- 5+ years of non-internship professional software development experience
- 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience programming with at least one software programming language
- Experience using big data technologies (Spark, EMR, Presto, etc.)