Senior Data Engineer

Forbes

. Own core business domains end to end (GTM, CX, and Finance), from stakeholder conversation to model, metric, and dashboard or AI answer .

Posted 10/14/2026full-timeTel Aviv • IsraelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in data engineering and analytics, with a strong focus on building and maintaining data pipelines, ensuring data reliability, and developing AI-driven data products. Proficient in SQL, Python, and cloud data warehousing, particularly with Snowflake and dbt.

Highest-signal resume keywords

Expert SQLCloud Data Warehouse (Snowflake)Data Pipeline DevelopmentProduction dbt Project OwnershipAI Data Product Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringAnalytics EngineeringPythonSQLDbtData Warehouse ManagementMetric DefinitionData ReliabilityIngestion Pipeline DesignAI Tools Utilization

Soft Skills

Clear CommunicationMentoringOpen-MindednessProblem-SolvingStakeholder Engagement

Tools & Technologies

FivetranAirbyteN8nSnowpipeAWSStripeLookMLCubeAirflowDagster

Industry Keywords

GTMCXFinanceRevenue RecognitionReconciliationIncident ResponseSemantic LayerData ProductsAI AssistantPerformance Monitoring

Tech Stack

Tools & technologies

AirflowAWSCloudPythonSQL

About the role

Key responsibilities & impact

Own core business domains end to end (GTM, CX, and Finance), from stakeholder conversation to model, metric, and dashboard or AI answer
Help build the data foundation with the founding engineer, including modeling patterns, incremental strategy, ingestion design, semantic-layer structure, and warehouse cost
Lead metric-definition work with business leaders, settle contested definitions, document them, and obtain sign-off
Own platform reliability, including testing strategy, monitoring and alerting, incident process, and root-cause fixes
Design and build ingestion for new sources using Fivetran, Airbyte, or custom n8n → S3 → Snowpipe pipelines
Reconcile Stripe payments and payouts to the source of truth and own revenue-data checks
Extend the semantic layer with definitions, descriptions, and verified queries
Build AI-first data products, including the AI data assistant, internal dashboards, and new data apps
Build evaluation sets to verify the correctness of AI answers
Set the standard for code review and mentor analysts shipping their own dbt changes
Use AI tools daily and build shared skills and workflows for the data team
Work directly with Finance, GTM, and CX leaders and analysts

Requirements

What you’ll need

5+ years in data engineering or analytics engineering, including at least 2 years owning a production data warehouse or dbt project
Expert SQL and deep experience with a cloud data warehouse (Snowflake preferred), including performance and cost
Designed and led a production dbt project: structure, conventions, testing, and CI
Built and run ingestion pipelines, with managed tools and with your own code
Owned data reliability: monitoring, alerting, incident response, and the fixes that follow
Led metric-definition work with senior non-technical stakeholders, such as Finance
Strong Python for pipelines, automation, and tooling
Review other people’s code and mentor less experienced colleagues
Use AI tools (Claude, Cursor, or similar) every day, with specific workflows you can describe and defend
Want to build data products with AI, not only use AI to write code
Open-minded: ask before assuming, and change your mind when evidence says so
Push back on the right questions, and make design decisions and own them
Clear, direct communicator, in writing and in conversation
Bonus: built or owned a semantic layer (Snowflake Semantic Views, dbt Semantic Layer, Cube, LookML, or similar)
Bonus: payments or financial data experience, including Stripe, revenue recognition, reconciliation, or month-end close
Bonus: shipped a data product with AI, such as an AI assistant, text-to-SQL, an LLM feature on data, or evaluation sets for AI answers
Bonus: orchestration and automation tools (n8n, Airflow, Dagster, or similar) and AWS

Benefits

Comp & perks

Hybrid work model (4 days in office, 1 WFH day)
Unlimited paid time off
Cibus meal card
Private health insurance subsidy
Pension
Keren Hishtalmut
Dmei Havraa
Real impact at scale — product used daily by thousands of paying customers
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