לוח משרות דרושים הייטק דאטה עבודה היברידית

Requirements:
Must-have:

1/2 years max! experience working with SQL databases (MySQL, SQL server, PostgreSQL).
Strong analytical skills with the ability to understand end-to-end (E2E) data processes and optimize workflows.
Excellent teamwork and communication skills to effectively collaborate with multi-functional teams.
Strong troubleshooting and problem-solving skills in data transformation and pipeline issues.
Strong ability to write high-performance, reusable SQL queries across multiple databases.
Degree in Industrial Engineering, Information Systems, or related field.


Advantage:

Russian - speaking is a big advantage
Experience working with large-scale data integration projects.
Experience with MongoDB, JavaScript, Groovy, or API development.
Familiarity with data streaming technologies and cloud-based integrations.
Background in customer-facing technical roles or working closely with CS teams

Requirements:
5+ years in data engineering.
Expertise in Apache Spark and Spark Structured Streaming.
Hands-on experience with Apache Iceberg or similar data lakes.
Proficiency in Scala, Java, or Python.
Knowledge of big data technologies (Hadoop, Hive, Presto).
Experience with cloud platforms (AWS, Azure, GCP) and SQL.
Strong problem-solving, communication, and collaboration skills.

Requirements:
7+ years of hands-on experience in machine learning and deep learning, with a strong focus on neural networks and NLP, including proven work on high-scale, production-level projects.
At least 3 years of proven experience in training, fine-tuning, and delivering LLMs and deep learning models at scale.
Advanced proficiency with Python and leading ML/DL frameworks: PyTorch, TensorFlow, Keras, etc.
Experience with MLOps, scalable model deployment, and optimizing inference for production environmnts.
Strong background in analyzing large, complex datasets using SQL, Spark, or similar tools.
Track record of delivering impactful AI solutions in real-world settings.
Experience in application/data security is a strong advantage.
Experience with cloud platforms (AWS, Azure, GCP) is a plus.
Experience with Kubernetes (K8s) and microservices infrastructure is a plus.
Experience with CI/CD practices and the full lifecycle of ML model development, from data preparation to deployment and monitoring, is a strong advantage.
Ph.D. or M.Sc. in Computer Science, Engineering, Math, or another quantitative/technical field is valued, but not required if you have strong relevant professional experience.
Excellent communication, mentoring, and collaboration skills.

Requirements:
We will only consider data engineers with strong coding skills with an extensive background in data orchestration, data warehousing and ETL tools.

Required qualifications:
Bachelor's or Master's degree in a related field (e.g. computer science, data science, engineering, computational biology).
At least 5 years of experience with programming languages, specifically Python.
Must have at least 3+ years of experience as a Data Engineer, ideally with experience in multiple data ecosystems.
Proficiency in SQL and experience with database technologies (e.g. MySQL, PostgreSQL, Oracle).
Familiarity with data storage technologies (e.g. HDFS, NoSQL databases).
Experience with ETL tools (e.g. Apache Beam, Apache Spark).
Experience with orchestration tools (e.g. Apache Airflow, Dagster).
Experience with data warehousing technologies (ideally BigQuery).
Experience working with large and complex data sets.
Experience working in a cloud environment.
Strong problem-solving and communication skills.
Familiarity with biotech or healthcare data - an advantage.

Desired personal traits:
You want to make an impact on humankind.
You prioritize We over I.
You enjoy getting things done and striving for excellence.
You collaborate effectively with people of diverse backgrounds and cultures.
You constantly challenge your own assumptions, pushing for continuous improvement.
You have a growth mindset.
You make decisions that favor the company, not yourself or your team.
You are candid, authentic, and transparent.

Requirements:
Masters degree in Statistics, Mathematics or Computer Science.
Minimum of 4 years of experience in designing, developing and deploying production-level deep learning recommendation models with a proven business impact.
Fluency in Python, Pandas/Dask, SQL, PyTorch or Tensorflow. Ability to write readable and maintainable code.
Strong communication and storytelling skills with both technical and non-technical audiences. Ability to present complex technical subjects to non-technical stakeholders.
Ability to read AI research publications and implement the algorithms & architectures from scratch.
Advantages
Advanced knowledge in generative models: Auto-encoding, adversarial models, compression.
Worked on deep learning graph model solutions with 10s of TB of data.
Publication in peer-reviewed conferences or journals on reinforcement learning, deep learning, and machine learning.
Strong passion for machine learning and investing independent time towards learning, researching, and experimenting with new innovations in the field.
Experience working with technologies like SageMaker, Athena/Trino, Spark, Milvus, and OpenSearch.

Requirements:
Masters degree in Statistics, Mathematics, or Computer Science.
5+ years managing a high-performing team of scientists and deploying production-level deep learning models with a proven business impact at scale.
Proven ability to conduct rigorous analysis and communicate conclusions to both technical and non-technical audiences.
Strong programming skills in Python with hands-on experience with deep learning tools such as PyTorch.
Fluency in SQL, preferably querying from a data lake.
Hands-on ability to read AI research publications and implement the algorithms & architectures from scratch.
Advantages
Publication in peer-reviewed conferences or journals on reinforcement learning, deep learning, and machine learning.
Presented your work in conferences or relevant meet-ups.
Worked with data at a scale of 100s of PB. Trained and deployed Gen-AI models at scale.
Experience working with technologies like Spark, SageMaker, Athena/Trino, Milvus, and OpenSearch.

Requirements:
8+ years of experience in data analysis with proven business impact preferably in the mobile apps market.
Strong expertise in SQL, Python, and data visualization tools (Tableau, Looker, Matplotlib).
Experience designing and analyzing A/B tests.
Familiarity with causality models for identifying relationships in data.
Advantages
Strong statistical background
Strong story-telling and presentation skills.

Requirements:
3+ years experience in data engineering or platform engineering roles
Strong programming skills in Python and SQL
Experience with orchestration platforms like Airflow/Dagster/Temporal
Experience with MPPs like Snowflake/Redshift/Databricks
Hands-on experience with cloud platforms (AWS) and their data services
Understanding of data modeling, data warehousing, and data lake concepts
Ability to optimize data infrastructure for performance and reliability
Experience working with containerization (Docker) in Kubernetes environments.
Familiarity with CI/CD concepts
Fluent in English, both written and verbal
And it would be great if you have (optional):
Experience with big data processing frameworks (Apache Spark, Hadoop)
Experience with stream processing technologies (Flink, Kafka, Kinesis)
Knowledge of infrastructure as code (Terraform)
Experience building analytics platforms
Experience building clickstream pipelines
Familiarity with machine learning workflows and MLOps
Experience working in a startup environment or fintech industry

Requirements:
1. Technical Expertise
Cloud Data Platforms: Expertise in Google Cloud Platform (GCP)
services, especially in data-related services like BigQuery, Cloud SQL,
Dataproc, Pub/Sub, and Dataflow.
Data Engineering: Strong knowledge of data pipelines, ETL/ELT
processes, and data integration using GCP tools.
Database Management: Experience with both relational and
non-relational databases, including MySQL, PostgreSQL, and NoSQL
solutions like Firestore or Bigtable.
Programming Skills: Proficiency in Python, SQL, and possibly other
languages like Java or Scala, with a focus on data manipulation and
processing.
APIs and Integrations: Experience working with Google APIs and
third-party APIs for data extraction and integration.
Machine Learning: Familiarity with Google Cloud AI tools, such as
Vertex AI, and how to integrate machine learning workflows into the data
architecture.
Data Governance: Knowledge of data security, privacy, governance, and
regulatory requirements (GDPR, HIPAA, etc.).
2. Leadership and Management

Team Management: Ability to lead a team of data engineers, analysts,
and architects, providing guidance, mentorship, and performance
management.
Project Management: Strong skills in managing data projects, ensuring
timely delivery while meeting client requirements.
Collaboration: Experience working cross-functionally with stakeholders
such as solution architects, developers, and business teams to translate
business needs into data solutions.
Resource Allocation: Efficiently managing cloud resources to optimize
cost and performance.
3. Client-Facing Skills
Consulting: Experience in providing consulting services to clients,
including understanding their data needs, providing solutions, and
guiding them through the data modernization process.
Pre-sales Support: Supporting sales teams in scoping out client
projects, preparing presentations, and explaining technical aspects of
data solutions during pre-sales.
Client Relationship Management: Building and maintaining strong
relationships with clients to ensure satisfaction and long-term
partnership.
4. Certifications and Education
● Google Cloud Certifications:
● Professional Data Engineer (preferred)
● Professional Cloud Architect (preferred)
● Other Certifications: Optional certifications in data engineering or
machine learning from organizations like Cloudera, AWS, or Microsoft
Azure can be advantageous.
● Educational Background: Bachelors or Masters degree in Computer
Science, Data Science, Engineering, or a related field.
5. Soft Skills
● Problem-Solving: Ability to troubleshoot data-related issues and
implement scalable solutions.
● Adaptability: Ability to keep up with the rapidly changing cloud and data
landscape.
● Communication: Excellent verbal and written communication skills to
explain complex data systems to non-technical stakeholders.

Requirements:
3+ years of direct experience in BI Development; writing complex and efficient Big Query queries.
2+ years of experience with BI tools (preferably Power BI).
Bachelors degree in computer science or related field.
Demonstrated advanced analytical, multi-tasking and problem-solving skills.
Direct experience with financial analysis a plus.