דרושים » דאטה » דרוש.ה מהנדס /ת נתונים בכיר /ה לארגון ממשלתי בירושלים

Requirements:
Excellent communication and collaboration skills
English at a high level, written and spoken required
Ability to work from our Jerusalem office (located in the Central Bus Station next to the train) 2 times a week (Monday & Wednesday) is required
Strong attention to detail, ownership mentality, and ability to work independently
Quick learner who can dive into new codebases, technologies, and systems independently
Hands-on mentality - not afraid to roll up your sleeves, dig into unfamiliar code, and work across the stack (including backend when needed)
4+ years of experience as a Data Engineer
Strong problem-solving skills for complex data challenges at scale - ability to debug performance issues, data inconsistencies, and system bottlenecks in high-volume environments
Experience with data modeling and schema design for analytical workloads
Strong proficiency in SQL and experience with complex analytical queries
Hands-on experience building and maintaining data pipelines (ETL/ELT)
Ability to troubleshoot and optimize systems handling large data volumes (millions+ rows, complex queries, high throughput)
Knowledge of query optimization techniques and execution planning
Familiarity with columnar databases (ClickHouse, BigQuery, Redshift, Snowflake, or similar). Columnar DB experience is a big plus.
Nice to Have
Experience with ClickHouse specifically
Experience with real-time or streaming data pipelines
Familiarity with SQL compilers or query engines
Background in data quality frameworks and observability tools
Experience with infrastructure as code (Terraform, Ansible, Pulumi ,etc.)
Contributions to open-source data projects
Experience with data orchestration tools (Airflow, Dagster, Prefect, etc.)
Experience with any scripting language for data processing
Understanding of distributed systems and data architecture concepts at scale

Requirements:
4+ years in data engineering or backend/software engineering with serious data work - pipelines that run in production, not just notebooks.
Strong Python and the PyData stack (NumPy, PyArrow, Pandas, DuckDB).
Some background in research, algorithms, or ML - enough that you can read a paper, understand a model's outputs, and have informed conversations with algorithm engineers.
Comfort working with vision-model outputs as data: embeddings, detection results, VLM responses.
Ability to work across team boundaries - this role lives between algorithm teams, infra teams, and our own.
Experience with autonomous-driving datasets or perception pipelines.
3D geometry and camera model intuition (or the mathematical background to ramp up).
Workflow orchestration (Argo, Airflow, Kubeflow).
Vector databases or columnar analytics (LanceDB, DuckDB, Parquet at scale).
Familiarity with curation concepts (active learning, hard-example mining, distribution balancing) - useful context, not a requirement.
Exposure to LLM agents or agentic workflows for data tasks.

Requirements:
4+ years in data engineering or backend/software engineering with serious data work - pipelines that run in production, not just notebooks.
Strong Python and the PyData stack (NumPy, PyArrow, Pandas, DuckDB).
Some background in research, algorithms, or ML - enough that you can read a paper, understand a model's outputs, and have informed conversations with algorithm engineers.
Comfort working with vision-model outputs as data: embeddings, detection results, VLM responses.
Ability to work across team boundaries - this role lives between algorithm teams, infra teams, and our own.
Experience with autonomous-driving datasets or perception pipelines.
3D geometry and camera model intuition (or the mathematical background to ramp up).
Workflow orchestration (Argo, Airflow, Kubeflow).
Vector databases or columnar analytics (LanceDB, DuckDB, Parquet at scale).
Familiarity with curation concepts (active learning, hard-example mining, distribution balancing) - useful context, not a requirement.
Exposure to LLM agents or agentic workflows for data tasks.