Data Engineer – Marketing & Performance Data (Azure, Python, Airflow)

Company Description Codem Inc. specializes in scalable, AI-driven eCommerce solutions tailored to transform businesses across industries such as Health & Beauty, Apparel, Consumer Electronics, and Home Goods. With expertise in launching and managing over 100 global B2C and B2B eCommerce platforms, the team draws on experience from industry leaders like Amazon, HP, McKinsey, and more. Operating from offices in San Francisco, Singapore, and Chennai, Codem delivers cost-efficient solutions by utilizing specialized teams and implementing advanced AI technologies. The company helps clients streamline operations, reduce costs, and make data-driven decisions through innovative tools like Augment for AI/ML applications, custom Shopify apps, and efficient DevOps services.

Role Overview

We are looking for a highly skilled

Data Engineer

with strong experience in building and maintaining

data pipelines for marketing and performance analytics , ideally within an

e-commerce environment . The role focuses on integrating

marketing and advertising data sources , building scalable pipelines, and enabling

reliable reporting for campaign performance, attribution, and cost analytics .

Key Responsibilities

Data Pipeline Development

Design, build, and maintain

robust data pipelines

for marketing and performance datasets Develop and enhance

API-based integrations

for external data sources Implement

Python-based ingestion and transformation workflows Data Integration & Processing

Integrate and process data from platforms such as: Google Ads Amazon marketing / advertising / marketplace APIs Handle

multiple data sources with inconsistent schemas and API limitations Optimize incremental data loads and transformation logic Orchestration & Workflow Management

Build and maintain workflows using: Apache Airflow Azure Synapse pipelines

(if applicable) Ensure

stable orchestration and scheduling of jobs Improve pipeline reliability and failure recovery mechanisms Cloud & Data Platform

Work within

Azure cloud environment Use Spark notebooks

for: Data transformation Processing workflows Operational data pipelines Build and maintain datasets in a

data warehouse (DWH)

environment Data Quality & Observability

Implement

data quality checks, monitoring, and alerting Troubleshoot: Pipeline failures API issues Schema changes Data discrepancies Improve

observability and scalability

of data systems Business Collaboration

Partner with

marketing and analytics teams

to: Understand campaign performance requirements Support attribution and reporting use cases Build datasets for: Marketing cost reporting Campaign performance analysis

Required Skills

Core Engineering Skills

Strong experience as a

Data Engineer Advanced Python

for: API integrations Data processing Pipeline development Strong

SQL skills Data Engineering & Architecture

Solid understanding of: Data warehousing concepts Data modelling ETL / ELT design Tools & Technologies

Apache Airflow Azure Cloud Spark / Spark Notebooks Experience with API-based data ingestion Marketing Data Experience

Hands-on experience with: Google Ads APIs Amazon marketing / marketplace data Experience building datasets for: Campaign performance Marketing cost reporting Data Operations

Experience with: Data quality validation Monitoring & alerting Troubleshooting production pipelines Preferred / Ideal Experience

Experience in: Performance marketing or attribution data engineering Handling

multi-source data pipelines with schema inconsistencies Optimizing: Incremental loads Cost-efficient data processing Pipeline orchestration reliability Direct collaboration with

marketing / analytics stakeholders Nice to Have

Experience with

retail media / marketplace data ecosystems Advanced experience with Spark Understanding of: Marketing attribution models Multi-touch attribution logic Candidate Profile

The ideal candidate is: Hands-on and technically strong Proactive in identifying and resolving issues Focused on pipeline reliability and scalability Business-aware with understanding of marketing metrics Structured and pragmatic in a production environment