דרושים » דאטה » Data Engineer

We are seeking a seasoned and execution driven VP, head of data platform to lead all the R&D tracks of the Data and Analytics organization in our company. This role focuses on leading a complex data engineering organization at scale, driving delivery, operational excellence and cross-company enablement through world-class data platforms and tools.
This leader will serve as the engineering owner of our data platform, managing multiple data infra, data engineering and BI development teams, overseeing extensive resources, and ensuring the delivery of high-quality, scalable, and reliable data products and services. This position requires a strong technical background combined with exceptional leadership, strategic thinking, and cross-functional collaboration skills.
He will also act as a key stakeholder in defining the architecture and strategic direction of our data infrastructure, pipelines, AI/ML infra and platforms to support the company's rapid growth and evolving business needs.
Hybrid
Full-time
What youll do: 
Lead and manage the entire data platform, including:
Real-Time Data & Streaming Infrastructure: Introduce and engineer robust data streaming infrastructures (e.g., using Kafka, Pub/Sub, Dataflow) to enable near-real-time data ingestion and scalable low-latency data serving, unlocking advanced analytics and critical use cases.
Data engineering teams responsible for the data ingestion, transformation and delivery pipelines
BI infra and development teams owning the cross-company consumption layer, including BI tools, BI data layers and dashboards
Serve as the operational and delivery lead across the data platform, ensuring strong project execution, roadmap alignment and measurable business impact
Data Engineering & Architecture Oversight: Lead the design, development, and evolution of scalable data platforms, encompassing data lakes, data warehouses, and advanced data products, ensuring they meet performance, reliability, and business requirements.
Operational Excellence & Reliability: Establish and drive engineering operational excellence processes across data engineering, significantly improving data quality, availability, and system reliability. Implement frameworks for proactive monitoring, alerting, and incident management, reducing major incidents and ensuring continuous visibility into data flows.
Advanced Data Observability: Integrate and leverage cutting-edge data observability solutions (e.g., Monte Carlo) to provide comprehensive visibility into data pipelines, enabling proactive detection and resolution of anomalies.
Cross-functional Collaboration & Stakeholder Management: Collaborate extensively with product, analytics, business, and infrastructure teams to align data strategies with overarching business priorities, ensuring the delivery of high-quality data products that meet diverse user needs.
Innovation & Technology Adoption: Stay abreast of the latest data, cloud, and AI trends, driving the evaluation and adoption of modern cloud-native technologies to continuously improve platform capabilities and future-proof the ecosystem.
Leadership & Team Growth: Lead, mentor, and grow a large team of data engineers, fostering a culture of technical excellence, continuous learning, and agile methodologies. Oversee budget management and drive talent development within the team.
Data Governance & Quality: Oversee the implementation of standards for data quality, data integrity tools, governance, privacy, and regulatory compliance across the data ecosystem.

We are seeking a Data Architect to lead the design of end-to-end, enterprise data architectures that enable analytics, AI, and data-driven transformation across customer environments.
In this role, you will act as a trusted advisor, defining the architectural vision, target-state designs, and roadmaps for IBM data solutions. You will work closely with customers, sales manager, and presale engineers to translate business needs into scalable, secure, and future-ready data platforms across hybrid and multi-cloud environments.
Responsibilities:
Define data architecture vision, target-state, and roadmaps for customer environments
Design enterprise-scale architectures for data warehouses, data lakes, lakehouse, and streaming platforms
Architect data platforms to support analytics, AI, and generative AI use cases
Act as a trusted advisor to customers on data modernization, governance, and AI readiness
Ensure architectures align with security, governance, and regulatory requirements
Guide implementation teams to ensure adherence to architectural standards
Collaborate with Consulting, Sales, and Delivery teams on solution design and proposals
Contribute to IBM reference architectures, standards, and reusable patterns

We are looking for an experienced, opinionated, and highly technical Staff Data Engineer to define the long-term direction of our Data Platform.
This role is ideal for a senior data leader who combines deep hands-on expertise with strong architectural judgment, enjoys mentoring others, and takes full ownership of complex, high-scale data systems.
You will drive platform architecture, set engineering standards, and lead the design and delivery of scalable batch and streaming data solutions on AWS.
Key Responsibilities:
Technical Leadership & Architecture:
Own and evolve the overall data platform architecture-scalability, reliability, security, and maintainability.
Lead the long-term strategic direction of the platform, balancing performance, cost, and operational excellence.
Introduce and drive adoption of modern data paradigms: lakehouse (Iceberg), event-driven pipelines, schema-aware processing.
Data Lake & Architecture Ownership:
Design, model, and evolve the Data Lake architecture, including:
Storage layout and data organization
Data formats and table design (e.g., Iceberg)
Batch and streaming ingestion patterns
Schema governance and lifecycle policies
Define and promote best practices for data modeling, partitioning, and data quality.
Ensure the Data Lake supports analytics, ML workloads, and operational systems at scale.
Platform Engineering:
Design and build high-scale ETL/ELT pipelines leveraging Apache Spark (EMR/Glue) and AWS-native services.
Optimize production-grade pipelines using S3, Athena, Kinesis, Lambda, Step Functions, and EKS.
Lead the rollout of modern patterns such as lakehouse architectures and event-driven data pipelines.
Security & Governance:
Ensure alignment with AWS security best practices-IAM, encryption, auditing, and compliance frameworks.
Partner with security and governance teams to support regulated and sensitive data environments.
Mentorship & Collaboration:
Serve as a technical mentor to data engineers; elevate team capabilities.
Lead architecture reviews and cross-team design discussions.
Work closely with Data Science, ML Engineering, Backend, and Product teams to deliver end‑to‑end data solutions.

We are seeking a Senior Backend & Data Engineer to join its SaaS Data Platform team.
This role offers a unique opportunity to design and build large-scale, high-performance data platforms and backend services that power our cloud-based products.
You will own features end to end-from architecture and design through development and production deployment-while working closely with Data Science, Machine Learning, DevOps, and Product teams.
What Youll Do:
Design, develop, and maintain scalable, secure data platforms and backend services on AWS.
Build batch and streaming ETL/ELT pipelines using Spark, Glue, Athena, Iceberg, Lambda, and EKS.
Develop backend components and data-processing workflows in a cloud-native environment.
Optimize performance, reliability, and observability of data pipelines and backend services.
Collaborate with ML, backend, DevOps, and product teams to deliver data-powered solutions.
Drive best practices, code quality, and technical excellence within the team.
Ensure security, compliance, and auditability using AWS best practices (IAM, encryption, auditing).
Tech Stack:
AWS Services: S3, Lambda, Glue, Step Functions, Kinesis, Athena, EMR, Airflow, Iceberg, EKS, SNS/SQS, EventBridge
Languages: Python (Node.js/TypeScript a plus)
Data & Processing: batch & streaming pipelines, distributed computing, serverless architectures, big data workflows
Tooling: CI/CD, GitHub, IaC (Terraform/CDK/SAM), containerized environments, Kubernetes
Observability: CloudWatch, Splunk, Grafana, Datadog
Key Responsibilities:
Design, develop, and maintain scalable, secure backend services and data platforms on AWS
Build and operate batch and streaming ETL/ELT pipelines using Spark, Glue, Athena, Iceberg, Lambda, and EKS
Develop backend components and data processing workflows in a cloud-native environment
Optimize performance, reliability, and observability of data pipelines and backend services
Collaborate with ML, backend, DevOps, and product teams to deliver data-driven solutions
Lead best practices in code quality, architecture, and technical excellence
Ensure security, compliance, and auditability using AWS best practices (IAM, encryption, auditing)

Our team is responsible for the data and data infrastructure that processes billions of records daily, driving critical business insights for both internal and external customers across the organization.
Our Data team consists of highly skilled senior software and data professionals who collaborate to solve complex data challenges. We process billions of records daily from multiple sources using diverse infra and multi-stage pipelines with intricate data structures and advanced queries, and complex BI.
A bit about our infrastructure. Our main databases are Snowflake, Iceberg on AWS, and Trino. Spark on EMR processes the huge influx of data. Airflow does most of the ETL.
The data we deliver drives insights both for internal and external customers. Our internal customers use it routinely for decision-making across the organization, such enhancing our product offerings.
What Youll Do
Build, maintain, and optimize data infrastructure.
Contribute to the evolution of our AWS-based infrastructure.
Work with database technologies - Snowflake, Iceberg, Trino, Athena, and Glue.
Utilize Airflow, Spark, Kubernetes, ArgoCD and AWS.
Provide AI tools to ease data access for our customers.
Integrate external tools such as for anomaly detection or data sources ingestion.
Use AI to accelerate your development.
Assures the quality of the infra by employed QA automation methods.