לוח משרות דרושים איש ביג דאטה Big Data

As a Data Engineer at Meta, you will shape the future of people-facing and business-facing products we build across our entire family of applications (Facebook, Instagram, Messenger, WhatsApp, Reality Labs, Threads). Your technical skills and analytical mindset will be utilized designing and building some of the world's most extensive data sets, helping to craft experiences for billions of people and hundreds of millions of businesses worldwide.

In this role, you will collaborate with software engineering, data science, and product management teams to design/build scalable data solutions across Meta to optimize growth, strategy, and user experience for our 3 billion plus users, as well as our internal employee community.

You will be at the forefront of identifying and solving some of the most interesting data challenges at a scale few companies can match. By joining Meta, you will become part of a world-class data engineering community dedicated to skill development and career growth in data engineering and beyond.

Data Engineering: You will guide teams by building optimal data artifacts (including datasets and visualizations) to address key questions. You will refine our systems, design logging solutions, and create scalable data models. Ensuring data security and quality, and with a focus on efficiency, you will suggest architecture and development approaches and data management standards to address complex analytical problems.

Product leadership: You will use data to shape product development, identify new opportunities, and tackle upcoming challenges. You'll ensure our products add value for users and businesses, by prioritizing projects, and driving innovative solutions to respond to challenges or opportunities.

Communication and influence: You won't simply present data, but tell data-driven stories. You will convince and influence your partners using clear insights and recommendations. You will build credibility through structure and clarity, and be a trusted strategic partner.
Data Engineer, Product Analytics Responsibilities
Collaborate with engineers, product managers, and data scientists to understand data needs, representing key data insights in a meaningful way
Design, build, and launch collections of sophisticated data models and visualizations that support multiple use cases across different products or domains
Define and manage Service Level Agreements for all data sets in allocated areas of ownership
Solve challenging data integration problems, utilizing optimal Extract, Transform, Load (ETL) patterns, frameworks, query techniques, sourcing from structured and unstructured data sources
Improve logging
Assist in owning existing processes running in production, optimizing complex code through advanced algorithmic concepts
Optimize pipelines, dashboards, frameworks, and systems to facilitate easier development of data artifacts
Influence product and cross-functional teams to identify data opportunities to drive impact

You will support our product and business data initiatives, expand our data warehouse, and optimize our data pipeline architecture.
You must be self-directed and comfortable supporting the data needs of multiple systems and products.
The right candidate is excited by the prospect of building the data architecture for the next generation of products and data initiatives.

This is a unique opportunity to join a team full of outstanding people making a big impact on Check Point.
We work on multiple products in many domains to deliver truly innovative solutions in the Cyber Security and Big Data realm.

This role requires the ability to collaborate closely with both R&D teams and business stakeholders, to understand their needs and translate them into robust and scalable data solutions.

Key Responsibilities
Maintain and develop enterprise-grade Data Warehouse and Data Lake environments
Create data infrastructure for various R&D groups across the organization to support product development and optimization
Work with data experts to assist with technical data-related issues and support infrastructure needs
Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, redesigning infrastructure for scalability
Build and maintain robust ETL/ELT pipelines for data ingestion, transformation, and delivery across various systems

At Similarweb, we are revolutionizing the way businesses interact with the digital world by revealing to them everything that happens online.

Our unique data and solutions empower over 4,300 customers globally, including industry giants like Google, eBay, and Adidas, to make game-changing decisions that drive their digital strategies.

In 2021, we went public on the New York Stock Exchange, and we continue to reach new heights! Come work alongside Similarwebbers across the globe who are bright, curious, practical, and good people.

Role Summary:
We are looking for a Data Engineer to join our team and play a key role in designing, building, and maintaining scalable, cloud-based data pipelines. You will work with AWS (Redshift, S3, Glue, Managed Airflow, Lambda) to integrate, process, and analyze large datasets, ensuring data reliability and efficiency.

Your work will directly impact business intelligence, analytics, and data-driven decision-making across the

What Youll Do:
ETL & Data Processing: Develop and maintain ETL processes, integrating data from various sources (APIs, databases, external platforms) using Python, SQL, and cloud technologies.
Cloud & Big Data Technologies: Implement solutions using PySpark, Databricks, Airflow, and cloud platforms (AWS) to process large-scale datasets efficiently.
Data Modeling: Design and maintain logical and physical data models to support business needs.
Optimization & Scalability: Improve process efficiency and optimize runtime performance to handle large-scale data workloads.
Collaboration: Work closely with BI analysts and business stakeholders to define data requirements and functional specifications.
Monitoring & Troubleshooting: Ensure data integrity and reliability by proactively monitoring pipelines and resolving issues.
Data Modeling: Design and maintain logical and physical data models to support analytics and operational needs