דרושים » ניהול ביניים » Data Team Lead

Requirements:
Requirements
5+ years of experience in data analysis, with at least 2 years in a leadership role.
Strong proficiency in SQL and experience working with analytical data warehouses (such as BigQuery and Snowflake) and BI tools (such as Looker and Tableau).
Bachelors degree in Engineering, Statistics, Computer Science, or a related field (or equivalent experience).
Ability to manage multiple projects, prioritize tasks, and meet deadlines in a fast-paced environment.
Excellent communication skills with the ability to translate complex data into actionable business insights.
Experience in hiring, mentoring, and leading an analytics team.

Bonus if you have:
Knowledge of scripting languages (Python, R), or machine learning concepts.
Familiarity with data engineering concepts and cloud-based data platforms.

Requirements:
At least 7 years of experience in backend or data engineering, with a minimum of 3 years in a team lead role.
Experience with development, deployment and automation on AWS - S3, SNS, lambda, batch, dynamo, RDS etc.
Expertise in advanced SQL, including writing complex, efficient queries and building optimized datasets.
Proficient in Python and Python-based data tools.
Working with micro-services on K8S
Proven track record of delivering large-scale, high-quality products
Independent, autodidact with excellent problem solving and passion to learn cutting-edge technologies
Proven ability to lead and mentor a team, manage projects, and collaborate effectively with cross-functional teams.
Outstanding interpersonal skills
Advantage:
Experience in building data stream pipelines and ETL using Big Data frameworks such as Spark and Apache Airflow
Experience with Serverless frameworks
Experience with streaming technologies such as Amazon Kinesis and Apache Kafka.

Requirements:
8+ years of software/data engineering (strong Python preferred; JVM/Go welcome).
4+ years working with large-scale data lakes/lakehouses, including Iceberg (or similar formats). Experience with Apache Spark for heavy batch / ML pipelines is a plus.
2+ years leading engineers, while still being hands-on.
Solid SQL skills and experience with much of our stack: Trino/Presto, dbt, Iceberg, Prefect/Airflow, Kubernetes, GitHub Actions.
A builders mindset: measure, improve, document, then automate.
Clear communication in English and a collaborative attitude.

Requirements:
2 years of experience in leading a data engineering team
Strong expertise in Apache Spark, and big data pipeline design.
Deep understanding of big data architecture in cloud environments, ideally GCP (e.g., Dataproc, BigQuery, GCS, Composer).
Problem-solving mindset with the ability to diagnose complex system behavior and debug issues across layers of the stack.
Experience with geospatial data or spatial analytics (Advantage).
Familiarity with Airflow (Advantage) Proficiency in Scala (Advantage)
Effective communicator with adaptability and prioritization skills to align cross-functional teams and balance short- and long-term goals.
BSc in Computer Science, Engineering or a related technical field (or equivalent experience in the 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:
5+ years of experience in Business Intelligence/Data Analysis/Data Engineering or a related field, with at least 2 years in a leadership or senior individual contributor role.
Expert-level proficiency in at least one leading BI tool (Power BI, Tableau, Looker) Must.
Deep understanding of data modeling, data warehousing concepts, database management, and SQL Must
Experience in working with a modern ETL Tool such as Airflow, dbt, NiFi, etc - Must
Python programming or other coding languages experience Must
Understanding DevOps Concepts, such as SSO, VNETs, and K8s - Must
Working with modern databases such as Snowflake, Big Query, Redshift Big Advantage
Experience using Cloud Providers Big Advantage
Working with big data Big Advantage
Fluent English and communication skills Must
Our Stack: Fabric (Power BI), Snowflake, Airflow, PostgreSQL, Python 3.

Requirements:
What you’ll bring:
? Bachelor’s degree in Computer Science, Engineering, or a related field; Master’s degree preferred. ? 3+ years of experience leading and managing a team of 5–8 engineers ? 8+ years of experience in the software engineering field. ? 5+ years of experience in big data technologies, with a focus on Elastic and Kafka. ? Proficiency in Java programming and experience with related frameworks. ? Strong understanding of data modeling, ETL processes, and data warehousing. ? Proven leadership skills with the ability to motivate and guide a team. ? Excellent problem-solving abilities and strong analytical skills. ? Strong communication skills, with the ability to convey technical concepts to non-technical stakeholders. ? A solid understanding of CI/CD principles. ? Experience working with both external and in-house APIs and SDKs Advantages:
? Experience working directly with customers ? Experience with Docker, Kubernetes ? Experience with cloud platforms (e.g., AWS or Azure)

Requirements:
B.Sc in computer science or equivalent.
Experience managing a team (preferably 3+ years) and leading projects hands-on from start to finish (preferably 8+ years, focusing on system architecture).
Proficiency in modern system architecture with large-scale and high performance.
Impact driven - use data to make sure every task moves the needle and utilize your resources to maximize the team's impact.
Team player, with strong communication skills and someone who thrives working in a fast-paced environment.
Creative, solution-oriented, and able to maintain a can do attitude, with the ability to work efficiently under pressure and uncertainty. Adapt quickly to changing business needs.
Passion for data.
Experience with microservices architecture
Fluent in English.
Preferred Experience:
Experience with software development in Python.
Experience with cloud technology.
Experience in managing data specialists (AI/ML engineers, analysts).

Requirements:
8+ years of experience in data engineering, software engineering, or MLOps, with hands-on leadership in designing modern data platforms and distributed systems.
Proven experience implementing GenAI applications or infrastructure (e.g., building RAG pipelines, vector search, or custom LLM integrations).
Deep understanding of big data technologies (Kafka, Spark, Iceberg, Presto, Airflow) and cloud-native data stacks (e.g., AWS, GCP, or Azure).
Proficiency in Python and experience with GenAI frameworks like LangChain, LlamaIndex, or similar.
Familiarity with modern ML toolchains and model lifecycle management (e.g., MLflow, SageMaker, Vertex AI).
Experience deploying scalable and secure AI solutions with proper attention to privacy, hallucination risk, cost management, and model drift.
Ability to operate in ambiguity, lead complex projects across functions, and translate abstract goals into deliverable solutions.
Excellent communication and collaboration skills, with a passion for pushing boundaries in both data and AI domains..