Backend Engineer II - Native Ads

Translate Adobe Analytics data into performance insights

Create unified reporting narratives

Identify and build marketing strategies via data analysis

Create test designs to quantify performance

Lead data initiatives and define models/pipelines

Own analyses guiding business and product decisions

Lead and prioritize data projects

Deliver insights for business decisions

Translate operational drivers into automated reporting metrics.

Architect long-range forecasting models with AI for 3–5 year roadmap.

Translate business drivers into automated reporting metrics

Build AI-assisted forecasting models

Own data models, dashboards, and reporting for the revenue funnel.

Use AI tools to accelerate insights and improve output quality.

Architect scalable data pipelines for healthcare data

Lead technical decision-making for data infrastructure

Build and maintain data pipelines with Fivetran, BigQuery, Sigma

Manage marketing tech stack and automate outbound campaigns

Standardize data infrastructure

Develop scalable data pipelines

Provide oversight and analysis of operational risk issues and controls

Assist with the implementation of Monzo's risk framework

Design and develop high-quality fullstack web solutions

Identify and solve complex, high-impact problems

Design, build, and maintain ETL/ELT pipelines

Develop and manage BigQuery data warehouse

Translate revenue data into executive insights

Design scalable dashboards and datasets for self-serve CFO/FP&A

Design, build, and own Hazel’s GTM stack

Build automated workflows for lead scoring, lifecycle marketing, and outbound prospecting

Understand customer data needs and goals

Build data pipelines and workflows

Maintain provider demographic data for network accuracy

Identify and resolve provider issues

Build scalable backend/services for Flourish platform

Collaborate with cross-functional teams

Build and scale ETL/ELT pipelines and compute infra across clouds.

Ingest and integrate new data sources for analytics and ML.

Develop reliable payment solutions

Implement AI to enhance payment success and reduce fraud

Design, build, and maintain integrations across Salesforce and marketing tools

Lead the design and implementation of scalable integrated solutions

Lead architecture and build high-scale data infrastructure and pipelines

Mentor engineers and promote data-engineering best practices

Define and drive long-term data architecture, modeling, and transformation

Establish reliability, observability, and quality standards across pipelines

Design, build, and maintain data pipelines

Develop, evaluate, and iterate ML models

Build scalable and reliable data systems

Collaborate with cross-functional teams

Support business teams with their operational needs

Define solutions with the Data Engineering team

Build backend systems for observability data transformation

Develop features for Portfolio Insights and product analytics

Architect, build, and maintain data models, pipelines, and infrastructure

Own end-to-end analytics engineering; report to the CTO

Lead design and development of real-time bidding systems

Build robust data pipelines for insights

Design and implement robust data models for analytics

Collaborate with stakeholders to deliver data products

Own end-to-end lifecycle of data platforms

Ensure scalability, reliability, and governance for data platforms

Iterate on features using Scala and React

Build core systems in a microservices architecture

Design and maintain production data models

Implement ELT in a cloud warehouse

Build data visualizations using Omni BI

Gather customer requirements and define product scope

Build and optimize batch pipelines for data orchestration

Develop and enhance BigQuery data models for analytics

Design, build, and maintain scalable data models with SQL and dbt

Apply software engineering practices: versioning, testing, CI

Build a high-performance cloud-native data platform

Create self-service data tooling for loading and testing

Build and operate data pipelines on Google Cloud Platform.

Contribute to integrations with external marketing platforms.

Design and implement data governance policies and procedures.

Build pipelines and warehouses for financial data reporting.

Design and implement IAM policies, audit logging, and scalable access for data governance.

Build and ensure data integrity for financial data infrastructure.

Own AI intake and delivery across Sales and CSG; define requirements and metrics

Build and ship AI workflows end-to-end; map processes and deploy agents

Analyze user behavior and share insights

Create KPIs, dashboards, and reports

Lead analytics initiatives and mentor data scientists

Collaborate with cross-functional teams

Design, build, and maintain production data models

Collaborate with Data Engineering

Architect data governance frameworks for compliance data risk

Enforce data contracts between Producers and Compliance/CX/IOS

Design and maintain production data models for incentives, promotions, and analytics.

Partner with Data Eng to model source data and implement auditable ELT.

Enable analytics for decision-making and data product development for the Client Coverage Group.

Translate data into strategy by analyzing client behavior and revenue.

Strengthen and automate data tools, systems, and processes

Design and maintain complex program data dashboards

Drive long-term growth and leverage latest technologies

Deliver actionable insights and recommendations

Analyze transactional data and financial performance to drive growth.

Communicate insights through visualizations and storytelling.