לוח משרות דרושים, לחיפוש זה נמצאו 34,782 משרות

Requirements:
Strong hands-on Apache Spark experience - building and operating pipelines in production, not just familiarity
Proficiency in PySpark or Scala for Spark development
Proven track record delivering ETL pipelines and data integration at scale
Solid SQL skills and command of data modeling concepts
Cloud platform experience (AWS, GCP, or Azure) in a production data context
Comfortable working with distributed systems and big data formats (Parquet, Delta Lake)
Nice to have:
Experience with pipeline orchestration tools, particularly Apache Airflow
Exposure to the geospatial or location analytics domain
Familiarity with Hadoop ecosystem components
Background in both Python and Scala (beyond Spark context)

Requirements:
8+ years of backend engineering experience
Prior experience with MCP servers, LLM tool use, or AI agent frameworks
Prior experience in data engineering or analytics tooling;
Solid understanding of REST APIs, OAuth 2.0, and credential management, including Google Cloud auth patterns (gcloud, service accounts)
Experience building and deploying services to Kubernetes or equivalent container infrastructure
Familiarity with Databricks or similar DW/DLs is a plus
Comfort working without an existing playbook - AI platform work at Placer is new; role definition, standards, and tooling will evolve
Strong communication skills; you'll regularly translate between business requests and engineering requirements
Comfortable navigating competing priorities across R&D Architecture (standards), AI Enablement (rollout), and business teams (requests)

Requirements:
Backend Expertise: 5+ years of backend engineering experience designing, operating, and scaling distributed microservices.
Data, ETLs & Databases: Hands-on experience architecting data-intensive systems, and high-throughput batch or real-time pipelines. Strong proficiency across diverse database technologies (relational, analytical warehouses like Postgres, Databricks, and Snowflake) with a track record of optimizing complex queries and data storage at scale.
Engineering Fundamentals: Strong foundation in core system design, data structures, clean API design, automated testing, and production observability (metrics, tracing, logging).
Cloud & Infrastructure: Excellent proficiency with cloud environments (AWS or GCP) and Infrastructure-as-Code (Terraform) for managing distributed execution environments.
AI Productivity: Demonstrated use of AI tools to work more efficiently, whether professionally or personally, and a curiosity for finding new ways to apply them. Comfort integrating generative AI into day-to-day workflows to boost productivity, quality, and output.
Education: BSc in Computer Science or equivalent practical experience.
NICE TO HAVE:
Python Services & Scripting: Strong proficiency with Python as a core backend language-building clean, modular microservice APIs (using frameworks like FastAPI or Flask), writing asynchronous tasks, and wrapping data processing logic into testable Python packages.
Distributed Processing with PySpark: Hands-on experience writing PySpark batch and streaming jobs to transform multi-terabyte datasets. Ability to optimize Spark execution plans, handle data skew, manage memory allocation, and minimize expensive shuffle operations across cluster nodes.

Requirements:
5+ years in data analytics / data research with large-scale datasets
3+ years hands-on Python and PySpark at scale
Solid applied statistics - sampling and estimation, bias and coverage, significance, uncertainty - and the ability to explain limitations to non-technical audiences
Demonstrated ownership of data quality: metrics and validations for datasets others depend on
Proficiency with AI-assisted coding (Claude Code, Cursor, Copilot), with judgment about when to trust, verify or discard output
Excellent written and spoken English; comfortable presenting to customers
Ability to own deliverables independently across multiple teams under tight deadlines
B.Sc. in a quantitative field; M.Sc. / MBA or academic research experience - advantage
Technical Skills: PySpark (must, with performance awareness), Databricks or equivalent Spark platform, Airflow in production, Terraform / IaC, Git-based collaborative development