Data Analytics Engineer (Maternity Cover)

Datarails | Financial software

17m ago

On-site

Full Time

Israel

Title: Data Analytics Engineer (Maternity Cover) Location: Tel Aviv-Yafo, Tel Aviv District, Israel

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.

Required Skills

sqlpythonbigqueryairflowgitdockerterraformci/cd

Required Languages

🇬🇧 English

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.