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Data Analytics Engineer (Maternity Cover)
Tel Aviv-Yafo, Tel Aviv District, Israel
Actively hiring, first seen by us 6 days ago- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
- Not stated
What you’ll work on
Full postingTurn business questions into metric definitions, KPIs and measurement plans
Build analysis and dashboards on acquisition, conversion, activation, feature adoption and retention
Identify tracking gaps and work with R&D to close them
Maintain and extend Airflow, Fivetran, connector and BigQuery pipelines and models
Monitor data quality and fix issues before they reach stakeholders
From the employer’s posting
Work with Revenue leaders to turn business questions into clear metric definitions, KPIs and measurement plans.
Build analysis and dashboards on acquisition, conversion, activation, feature adoption and retention.
Identify gaps in tracking and work with R&D to close them.
Maintain and extend existing pipelines and models (Airflow, Fivetran, ad platform connectors, BigQuery transformations).
Monitor data quality and fix issues before they reach stakeholders.
What you’ll bring
All qualificationsCore experience
- 3+ years as an analytics engineer or data analyst, preferably in B2B SaaS.
- Data modeling and pipeline experience with tools such as Fivetran, Funnel.io or dbt.
- Experience defining metrics and KPIs with business stakeholders, and specifying and building the dashboards that track them.
- Familiarity with HubSpot and Salesforce data structures.
- Experience working across business domains (such as marketing, sales, product or finance), and quickly learning a new team’s data and questions.
- Understanding of SaaS metrics (ARR, churn, CAC, LTV).
Qualification wording
3+ years as an analytics engineer or data analyst, preferably in B2B SaaS.
Data modeling and pipeline experience with tools such as Fivetran, Funnel.io or dbt.
Experience defining metrics and KPIs with business stakeholders, and specifying and building the dashboards that track them.
Familiarity with HubSpot and Salesforce data structures.
Experience working across business domains (such as marketing, sales, product or finance), and quickly learning a new team’s data and questions.
Understanding of SaaS metrics (ARR, churn, CAC, LTV).
Posting record
Report an errorActively hiringFirst seen by us 6 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Oct 29, 20266 days ago
- Recorded sightings
- 7
- Last seen by us
- Nov 4, 2026
- Employer says posted
- Oct 26, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Datarails is hiring
1 open job
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordSee how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- SQL
- BigQuery
- dbt
- Docker
- Fivetran
- GCP
- Looker
- Terraform
- Airflow
Where the posting names them
- Experience working across business domains (such as marketing, sales, product or finance), and quickly learning a new team’s data and questions. - Proficiency in SQL and Python for data processing, analysis and automation. - Data modeling and warehousing experience in a cloud data warehouse, ideally BigQuery on GCP.
We are looking for a Data Analytics Engineer with strong technical skills to join the Marketing Data team. You will be the go-to person for how Marketing and other teams measure what matters. You will define the metrics, build the analysis behind them, and keep the data underneath them reliable. Our marketing data model on BigQuery is already in production. You will report to the VP Growth and work directly with Marketing, Product and Sales leaders. What You'll Do
- Identify gaps in tracking and work with R&D to close them. - Maintain and extend existing pipelines and models (Airflow, Fivetran, ad platform connectors, BigQuery transformations). - Monitor data quality and fix issues before they reach stakeholders.
- Proficiency in SQL and Python for data processing, analysis and automation. - Data modeling and warehousing experience in a cloud data warehouse, ideally BigQuery on GCP. - Hands-on experience building and maintaining ETL/ELT pipelines from multiple sources, orchestrated with Apache Airflow.
- Marketing analytics background: attribution, funnel and campaign performance. - Data modeling and pipeline experience with tools such as Fivetran, Funnel.io or dbt. - Familiarity with HubSpot and Salesforce data structures.
- Ownership of data observability: pipeline health, freshness and quality. - Git and version-controlled development (branches, pull requests, code review), with familiarity with CI/CD, Docker and Terraform. - Works independently: can take a loosely defined question, scope it, and deliver without close technical supervision.
- Understanding of SaaS metrics (ARR, churn, CAC, LTV). - Experience with Looker Studio or another BI tool.
- Data modeling and warehousing experience in a cloud data warehouse, ideally BigQuery on GCP. - Hands-on experience building and maintaining ETL/ELT pipelines from multiple sources, orchestrated with Apache Airflow. - Ownership of data observability: pipeline health, freshness and quality.
Your next step
Complete your application on job-boards.greenhouse.io. The employer’s form will show what is required.
Applying through Greenhouse
- No account needed: the form is one page, with an “Autofill my application” option that reads your resume.
- Greenhouse doesn’t read resumes over 2.5 MB, or details placed in a header, footer, text box, table or second column. The file still attaches, but a recruiter has to type you in by hand.
Already applied? Track this application
About the role
We file this job under Analytics Engineer, by the work the posting describes.
Analytics engineers turn raw warehouse data into clean, tested and documented tables that analysts and dashboards can rely on, often with dbt.
Expect strong SQL, data modeling, Git, and a warehouse such as Snowflake, BigQuery or Redshift.
A data engineer gets data into the warehouse; an analytics engineer shapes it once it is there and works closer to the analysts who use it.
US government figures for database architects
- Median yearly pay, May 2025
- $139,500
- Expected change, 2025 to 2035
- 9.4% more jobsagainst 3.5% for all jobs
The government has no code for analytics engineers, so these figures are for the closest group, database architects, the same group we use for data engineers.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
Job description
Datarails is the AI operating system for finance teams. We unite financial and operational data through FinanceOS, the trusted data layer for finance, and build the applications, workflows and AI experiences finance teams use to run the business.
We are looking for a Data Analytics Engineer with strong technical skills to join the Marketing Data team. You will be the go-to person for how Marketing and other teams measure what matters. You will define the metrics, build the analysis behind them, and keep the data underneath them reliable.
Our marketing data model on BigQuery is already in production. You will report to the VP Growth and work directly with Marketing, Product and Sales leaders.
What You'll Do
- Work with Revenue leaders to turn business questions into clear metric definitions, KPIs and measurement plans.
- Build analysis and dashboards on acquisition, conversion, activation, feature adoption and retention.
- Identify gaps in tracking and work with R&D to close them.
- Maintain and extend existing pipelines and models (Airflow, Fivetran, ad platform connectors, BigQuery transformations).
- Monitor data quality and fix issues before they reach stakeholders.
- Document metric definitions, models and decisions as you go.
Requirements
Must have
- 3+ years as an analytics engineer or data analyst, preferably in B2B SaaS.
- Experience defining metrics and KPIs with business stakeholders, and specifying and building the dashboards that track them.
- Experience working across business domains (such as marketing, sales, product or finance), and quickly learning a new team’s data and questions.
- Proficiency in SQL and Python for data processing, analysis and automation.
- Data modeling and warehousing experience in a cloud data warehouse, ideally BigQuery on GCP.
- Hands-on experience building and maintaining ETL/ELT pipelines from multiple sources, orchestrated with Apache Airflow.
- Ownership of data observability: pipeline health, freshness and quality.
- Git and version-controlled development (branches, pull requests, code review), with familiarity with CI/CD, Docker and Terraform.
- Works independently: can take a loosely defined question, scope it, and deliver without close technical supervision.
Nice to have
- Marketing analytics background: attribution, funnel and campaign performance.
- Data modeling and pipeline experience with tools such as Fivetran, Funnel.io or dbt.
- Familiarity with HubSpot and Salesforce data structures.
- Understanding of SaaS metrics (ARR, churn, CAC, LTV).
- Experience with Looker Studio or another BI tool.
Source details
Source notes
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- Pay
No pay amount identified in the saved description.
- Location & working pattern
Tel Aviv-Yafo, Tel Aviv District, Israel
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.