לוח משרות דרושים הייטק דאטה עבודה היברידית, לחיפוש זה נמצאו 48 משרות

Requirements:
Requirements:
5+ years of hands-on experience building production data solutions for SaaS organizations on Snowflake as a Data Engineer, BI Developer, Analytics Engineer, or similar role.
Strong experience with Snowflake objects and capabilities, including warehouses, databases, roles, schemas, views, stored procedures, tasks, streams, and performance tuning.
Experience building reliable ELT or ETL pipelines using incremental loads, CDC, backfills, reconciliation, and idempotent processing.
Strong dimensional modeling and data warehousing foundations, including facts, dimensions, star schemas, marts, and reusable business entities.
Salesforce data experience, including core objects, relationships, API limitations, and Salesforce-to-warehouse pipelines.
Hands-on experience with Snowflake Cortex capabilities such as Cortex Analyst, Cortex Search, Cortex LLM functions, and governed AI data access patterns.
Experience building AI-ready semantic models and curated datasets for governed analytics, BI, and enterprise AI use cases.
Understanding of secure enterprise AI integration patterns, including governed structured data access and MCP-based integrations.
Strong knowledge of data governance, privacy, security, RBAC, masking policies, row access policies, secure views, and compliance-aware data handling.
Proven Snowflake FinOps experience, including credit monitoring, warehouse sizing, workload optimization, storage awareness, and query cost reduction.
Strong system analysis, requirements gathering, source-to-target mapping, lineage, impact analysis, and stakeholder communication skills.

Nice to have:
Python for data processing, automation, testing, or operational tooling.
AWS cloud data experience.
Experience with Snowpark or Snowpark ML.
Experience with BI tools such as Sisense, Tableau, or Power BI.
Experience with data observability, orchestration, CI/CD, or automated testing for data pipelines.

Requirements:
Requirements: must have requirements: • Bachelor’s degree in Computer Science, Data Science, Statistics, or another relevant field, or equivalent professional experience. • At least 5 years of experience in Data Engineering, with a focus on Analytics and BI. • Expert-level SQL skills. • Deep experience with PostgreSQL, including performance tuning and optimization. • At least 3 years of experience with Python for data processing, automation, and testing. • At least 3 years of experience designing and implementing large-scale data models. • Experience leading performance optimization in complex data systems. • Experience establishing and implementing Data Governance and Data Quality processes. • Proven ability to translate business requirements into data architecture and technological solutions. • Experience providing technical leadership, mentoring, and leading technological initiatives. • Experience building Data and Analytics platforms from scratch. • Proven experience integrating AI and LLM tools into development and data engineering processes. Advantages: • Experience in FinTech or financial organizations. • Deep familiarity with regulation, information security, and compliance requirements. • Experience working with Data Lakes and Data Warehouses. • Experience with orchestration tools and ELT / ETL processes. • Experience working in cloud environments such as AWS, GCP, or Azure. • Familiarity with streaming technologies and real-time data processing. • Experience leading technology teams or professional excellence groups. Personality requirements: • Fast learning ability and curiosity for new technologies and methodologies. • Strategic thinking and broad system-wide perspective. • Ability to lead and influence without formal authority. • Ability to work independently and manage multiple tasks simultaneously. • High personal responsibility and ability to receive professional feedback. • Initiative, creativity, and ability to solve complex problems. • High motivation and constant drive for excellence. • Excellent interpersonal communication skills and ability to work with multiple stakeholders. • Ability to driv

Requirements:
3+ years of engineering management experience leading Data / ML / Software engineering teams in production environments.
6+ years of experience building large-scale distributed systems in Data Engineering, ML Engineering, or Software Engineering roles.
Proven ownership of production-grade data or ML platforms, including delivery and adoption across R&D and Product stakeholders.
Hands-on experience building and operating high-scale distributed data systems (Spark, Storm, Flink) in production.
Strong experience with Java and Python in AWS cloud environments.
Advantages:
Proven track record leading multi-disciplinary teams and driving measurable business impact through data/ML systems.
Experience building ML platforms, feature stores, or self-serve data infrastructure at scale.
Deep experience with modern ML/infra stack (PyTorch, TensorFlow, SageMaker, Kubernetes, Argo).
Experience with modern data lakehouse and analytics stack (Iceberg, Athena, ClickHouse, data catalogs, data quality frameworks).
Experience deploying LLM-based systems or AI-driven infrastructure in production environments.

Requirements:
Interested? Here's what we're looking for:

5+ years of experience in fintech or payments fraud analytics. You are a seasoned IC who has scaled fraud programs in complex, high-volume environments.

AI/ML Expertise: Proven experience leveraging Generative AI, LLMs (e.g., RAG, agentic workflows), and Machine Learning to solve real-world risk problems. You don't just use these tools; you know how to build with them.

Technical Powerhouse: Mastery of Python (for data science and automation) and SQL (performance tuning, CTEs, window functions). You are comfortable working alongside engineers and data scientists.

Network Analysis: Deep experience with graph databases (Neo4j, TigerGraph) and link-analysis techniques to identify organized criminal networks.

Demonstrated ability to translate complex quantitative findings into executive‑level insights and influence roadmaps across product, engineering, finance, and support.

Strategic Influence: While this is not a management role, you have a track record of influencing technical roadmaps and advising senior leadership on risk-reward trade-offs.

Advanced data‑visualization skills (Looker, Tableau, Omni, Superset, or equivalent) with a portfolio of self‑service dashboards adopted by the company‑wide.

Domain Depth: Expert knowledge of the payments lifecycle, including CNP, ACH, and RTP risk, as well as the specific fraud challenges of the SaaS and marketplace sectors.

Regulatory Fluency: A solid understanding of PCI DSS, Nacha rules, and the emerging regulatory landscape surrounding AI in financial services.

Prior experience scaling fraud programs in a marketplace, SaaS, or creator‑economy context - Advantage.

Education: Bachelors or Masters degree in a quantitative field (e.g., CS, Statistics, Mathematics, Economics) or equivalent technical experience- Advantage.

Requirements:
3+ years of hands-on experience with AWS, including solution design, migration, and production systems
2+ years in customer-facing technical roles, for example Cloud Architect, SRE, or Customer Engineer, working directly with stakeholders
Solid understanding of modern data architectures, including batch, streaming, and lakehouse patterns
Practical experience with core AWS data services, such as S3, Glue, Redshift, Kinesis or MSK, EMR, Athena, Lambda, and IAM
Familiarity with broader AWS infrastructure, including networking, IAM, and compute patterns
Experience working with real-time or near real-time data processing
Strong grasp of data modeling, pipelines, and storage optimization
Ability to reason about trade-offs, not just select tools
Ability to lead end-to-end technical engagements, from discovery through delivery
Comfort operating in fast-moving environments with ambiguous requirements
Clear communication skills, able to explain technical decisions to both technical and non-technical stakeholders
Strong communication skills in English. Spanish is a plus
Basic understanding of security and access control in AWS environments
Experience writing HLDs, SOWs, and supporting pre-sales engagements
Experience with ETL processes, data lakes, and pipelines
Nice to Have
Experience with Iceberg, dbt, or similar modern data tooling
Familiarity with Kubernetes, Docker, Terraform, and CI/CD pipelines
AWS certifications (Solutions Architect, Data Analytics, or similar)

Requirements:
What You Bring
Bachelor's degree in Computer Science, Software Engineering, Data Engineering, Information Systems, or a related engineering discipline, or equivalent practical experience.
5+ years of professional software engineering experience, including substantial experience building backend systems, distributed systems, or data-intensive applications in production environments.
Strong Python engineering skills, including modular architecture, dependency management, testing practices, observability, and production-grade code quality.
Strong SQL and data modeling expertise, including schema design, indexing strategies, event-driven data models, and scalable analytical aggregations.
Hands-on experience building incremental and idempotent data pipelines that handle duplicate, invalid, and late-arriving events without impacting downstream consumers.
Experience with at least one major cloud platform (Azure, GCP, or AWS) and modern lakehouse technologies such as Databricks, Delta Lake, Spark, or equivalent architectures.
Experience with streaming or messaging technologies such as Kafka, Pub/Sub, Event Hubs, or similar event-driven systems.
Proven ability to diagnose and resolve production data issues, including data quality problems, schema evolution, backfills, replay scenarios, and performance bottlenecks.
Strong written and verbal communication skills in English and experience collaborating effectively with globally distributed teams.

Nice to Have
Experience building industrial, IoT, manufacturing, or operational data platforms.
Familiarity with Digital Twin architectures and industrial data models.
Experience with graph databases, context graphs, knowledge graphs, or relationship-centric data modeling.
Exposure to AI/LLM-powered applications, including retrieval-augmented generation (RAG), agents, tool calling, or evaluation frameworks.
Experience working with Databricks or similar lakehouse platforms from both application and platform perspectives.
Experience building data products that directly support AI agents, intelligent applications, or machine learning workflows.

Requirements:
3+ years of hands-on experience working with AI - building AI-powered solutions, agents, automations, or AI-driven workflows in a professional setting.
3+ years in roles requiring both technical depth and business acumen - such as solution architecture, systems design, technical consulting, product, operations, CS Ops, customer operations, or technology-driven business transformation.
Strong understanding of Customer Success, support operations, customer operations, or post-sale customer journeys.
Ability to translate business problems into practical AI solutions that are adopted by real users.
Strong technical and architectural thinking, including workflow design, integrations, prompt logic, agent behavior, automation, and scalable systems.
Understanding of AI solution design, including prompt engineering, context engineering, agent design, orchestration, workflow automation, integrations, and runtime behavior.
Experience working with operational systems such as Zendesk, Slack, Notion, CRM, knowledge bases, data tools, workflow automation platforms, or similar tools.
Strong execution mindset, with the ability to move from problem definition to prototype, rollout, measurement, and continuous improvement.
Excellent communication and stakeholder management skills, including the ability to present to leaders, facilitate workshops, coach teams, and translate technical concepts into business language.
Strong analytical skills, with the ability to define KPIs, identify patterns, measure impact, and improve performance over time.
High adaptability, curiosity, and comfort working in ambiguity as AI tools and business needs evolve.
Technical education or equivalent practical experience in Computer Science, Information Systems, Industrial Engineering, Data, Operations, or a related field.

Requirements:
5+ years of experience in BI / Data Engineering roles with ownership of scalable data platforms
Deep experience with modern data stacks (Snowflake or Databricks, dbt)
Advanced SQL and Python skills, including data quality, CI/CD, and observability
Strong understanding of dimensional modeling, data warehousing, and semantic layers
Experience with orchestration tools (Airflow) and large-scale data processing
Proven experience using GenAI tools as part of your day-to-day development workflow
A strong builder mindset, business orientation, and ability to lead cross-functional initiatives
Nice to have:
Experience with streaming technologies (Kafka, Spark).

Requirements:
5+ years in data engineering, data analytics, or product analytics
Strong SQL and hands-on experience with large-scale datasets in cloud data warehouses (BigQuery or similar)
Production Python experience for data pipelines
Solid grounding in product metrics, funnels, and user behavior analysis
Ability to turn business questions into data models, metrics, and dashboards
Strong communication and cross-functional collaboration skills
Nice to Have:
Streaming or event-driven data systems
Product instrumentation and tracking design
AI/ML or LLM experience
High-scale SaaS or consumer product environments.

Requirements:
6+ years of software engineering experience building production-level systems.
2+ years of engineering management or technical leadership experience.
Strong experience building large-scale backend systems in Python.
Experience developing modern web applications using frameworks such as React / Next / Angular / Vue.
Experience deploying and operating LLM-based systems in production, including evaluation and iteration.
Strong understanding of data pipelines, experimentation, and product analytics.
Experience with modern cloud environments such as Google Cloud Platform or Amazon Web Services.
Passion for clean code, scalable architectures, and data-driven product development.
Experience with prompt engineering, RAG architectures, and vector databases.
Experience building AI agents or autonomous workflows
Nice to Have:
Experience with frameworks such as ADK, A2A, LangChain, LangGraph, or LlamaIndex or equivalent
Experience with gRPC and protobuf-based architectures
Experience building MCP servers
Background in data engineering, experimentation platforms, or ML infrastructure.

Requirements:
What will you bring to the team?
Your skills and experiences:
Strong communication skills, working collaboratively with the team, able to teach concepts, and communicate clearly to a wide audience of complicated topics.
Strong problem solving skills, and deducing from specific problems wider range products.
True scientist skills: fast learner, curious, sense of details, rigorous.
Experience in Statistics (i.e. statistical analysis, regression analysis, ) and Artificial Intelligence (i.e. Data Mining, Machine Learning, ).
An appetite for Data Science applied to high-volumetry, low-latency topics.
Ability to read scientific articles, to analyze critically, and to implement as appropriate.
Good programming abilities.
You are very mindful about your application architecture, performance, testing and maintainability and its overall quality.
Requirements:
5+ years of hands-on experience with coding ML based solutions in high scale systems
Relevant industry experience / publications in ad-tech and recommender system
Msc/Phd in computer science / math
Industry experience in building engineering at scale
Large-scale distributed systems, service oriented architecture.
Previous experience with all or some of the elements of our Stack (we mainly use Go, Python, Java, AWS, Jupyter notebooks).
Knowledge in data engineering.
Ability to transform raw data into actionable business insights
Please submit your CV in English.

Requirements:
5+ years of data engineering experience, building and operating production data pipelines at scale (TB+ datasets, hourly/daily batch or streaming workloads).
Hands-on production experience with Apache Spark and distributed data processing frameworks such as Flink, Hive, or Trino. Strong understanding of large-scale batch and streaming pipelines, including performance tuning and troubleshooting. Language is not a filter: Scala, Python, or Java are all fine. What matters is that you can debug and ship production Spark code, not which language you write it in
Production experience building and operating data solutions on GCP or AWS, including cloud-native services such as BigQuery, Dataproc, GCS, S3, EMR, or Redshift. Experience across the full project lifecycle is preferred.
Production experience with Kafka or Kafka-compatible streaming platforms, including the development, operation, and troubleshooting of real-time data pipelines, as well as debugging production incidents involving consumer lag, partition rebalancing, or data loss.
Strong understanding of data warehouse and data lake concepts, including Medallion Architecture (Bronze, Silver, Gold) and data platform best practices.
Nice to have
Production experience with a lakehouse table format (Apache Iceberg or Delta Lake). The specific format matters less than the underlying concepts: you understand how the lake stores data on object storage, how a table format adds transactional semantics on top and how a data warehouse can read that data. You have dealt with schema evolution and table maintenance.
Please submit your CV in English.

Requirements:
4+ years of proven experience leading product teams, with a strong track record of growing people and helping them succeed.
Startup mindset: comfortable with ambiguity, fast growth, and evolving processes, while maintaining focus on scalable solutions.
Proven experience leading B2B products and collaborating with partners and strategic customers to deliver impactful solutions.
Strong technical orientation and curiosity, with the ability to quickly understand complex data systems.
Excellent communication and stakeholder management skills, able to explain complex technical concepts in clear, actionable terms.
Experience leading large-scale data platforms, including data ingestion, transformation, and access layers.
Ability to define and track success metrics, analyze performance, and iterate to optimize platform impact.
Advantages:
Familiarity with healthcare data ecosystems (EMRs, HIEs, clinical data).

Requirements:
6+ years of experience in software development, with at least 4-5 years as a data engineer.
Proven track record designing and implementing scalable ETL/ELT data pipelines.
Strong SQL skills.
Experience with relational and analytical data platforms (e.g., PostgreSQL, Snowflake, data lakes).
Strong coding skills (preferably in Python), with prior software engineering experience (e.g., API development) a plus.
Experience with cloud environments, AWS preferable
Previous managerial or leadership experience is a strong advantage

Requirements:
6-10+ years of experience in Data Engineering, with demonstrated architectural ownership.
Expert-level experience with Snowflake (mandatory), including performance optimization, data modeling, security, and ecosystem components.
Expert proficiency in SQL and strong Python skills for pipeline development and automation.
Experience with modern orchestration tools (Airflow, Dagster, Prefect, or equivalent).
Strong understanding of ELT/ETL patterns, distributed processing, and data lifecycle management.
Familiarity with streaming/event technologies (Kafka, Kinesis, Pub/Sub, etc.).
Experience implementing data quality, observability, and lineage solutions.
Solid understanding of cloud infrastructure (AWS, GCP, or Azure).
Strong background in DataOps practices: CI/CD, testing, version control, automation.
Proven leadership in driving architectural direction and mentoring engineering teams
Nice to Have:
Experience with data governance or metadata management tools.
Hands-on experience with DBT, including modeling, testing, documentation, and advanced features.
Exposure to machine learning pipelines, feature stores, or MLOps.
Experience with Terraform, CloudFormation, or other IaC tools.
Background designing systems for high scale, security, or regulated environments.