Data Engineer Technology

Earnmodes; Bengaluru; On-site; INR 2,800,000 - 4,200,000; Full time

Job summary

Earnmodes in Bengaluru is seeking a Senior Data Engineer to strengthen the Data Engineering ecosystem for Content Ops and Site Quality. The role focuses on building and optimizing large-scale data pipelines, data models, analytics infrastructure, and AI-driven solutions that empower business and analytics teams.

The ideal candidate will have hands-on SQL and PySpark expertise, experience with SQL Server, SSAS, Airflow, Iceberg, and cloud/container platforms, plus familiarity with Adobe Analytics

Qualifications

• Strong hands-on experience in Data Engineering with SQL and PySpark.
• Experience with SQL Server, SSAS, Airflow, and modern data platforms.
• Experience working with Iceberg and cloud/container-based environments such as ECS.
• Strong understanding of data modeling, ETL/ELT, data pipelines, and data quality.
• Experience with Adobe Analytics or similar digital analytics platforms.
• Exposure to AI/LLM solutions, RAG, AI agents, or Generative AI applications.

Responsibilities

• Own and manage Data Engineering activities supporting Content Ops and Site Quality.
• Develop, maintain, and optimize data pipelines supporting large-scale datasets.
• Build and maintain scalable data processing solutions using SQL Server, Iceberg, PySpark, ECS, and Airflow.
• Troubleshoot data quality, pipeline, performance, and production issues and drive them to resolution.
• Support data modeling and development of reliable datasets for downstream analytics and reporting.
• Develop and enhance AI/LLM-based solutions, including RAG and agent-based applications.

Skills

SQL

PySpark

Data Modeling

ETL/ELT

Airflow

Cloud Platforms

Adobe Analytics

AI/LLM

Problem Solving

Tools

SQL Server

SSAS

Airflow

Iceberg

ECS

PySpark

Job description

Job Description

About the job
Senior Data Engineer – Content Ops & Site Quality

Role Overview

We are looking for a Senior Data Engineer to support the Data Engineering ecosystem for Content Operations and Site Quality . The role will involve managing and enhancing large-scale data pipelines, data models, analytics infrastructure, and AI-driven solutions that support business and analytics teams.

The ideal candidate will have strong hands-on experience in SQL, PySpark, data engineering, cloud/data platforms, and a working understanding of Adobe Analytics and AI/LLM-based solutions.

Key Responsibilities

Data Engineering & Platform Management (Must have)

Own and manage Data Engineering activities supporting Content Ops and Site Quality.
Develop, maintain, and optimize data pipelines supporting approximately 150 MDP tables, 2 SSAS Cubes, and multiple Airflow workflows.
Build and maintain scalable data processing solutions using SQL Server, Iceberg, PySpark, ECS, and Airflow.
Troubleshoot data quality, pipeline, performance, and production issues and drive them through to resolution.
Support data modeling and development of reliable datasets for downstream analytics and reporting.
AI & Analytics Solutions (Preferred)

Develop and enhance AI/LLM-based solutions, including RAG and agent-based applications.
Contribute to an AI agent/RAG solution that enables users to translate Adobe Analytics requirements into SQL queries.
Maintain and enhance the Client’s chatbot assistant, including updates, new capabilities, and ongoing performance improvements.
Identify opportunities to leverage AI and automation to simplify data and analytics workflows.
Stakeholder & Solution Development

Partner with business and analytics stakeholders to understand requirements and translate them into scalable technical solutions.
Contribute to solution design, technical discussions, and roadmap development.
Clearly communicate complex technical concepts and solutions to non-technical stakeholders.
Required Skills & Experience

Strong hands-on experience in Data Engineering with SQL and PySpark.
Experience with SQL Server, SSAS, Airflow, and modern data platforms.
Experience working with Iceberg and cloud/container-based environments such as ECS.
Strong understanding of data modeling, ETL/ELT, data pipelines, and data quality.
Experience with Adobe Analytics or similar digital analytics platforms.
Exposure to AI/LLM solutions, RAG, AI agents, or Generative AI applications.
Strong problem-solving and troubleshooting skills.
Ability to work independently and manage multiple production-critical data assets.
Preferred Skills

Experience building RAG/agentic AI applications using LLMs.
Experience converting business/analytics requirements into SQL and data solutions.
Experience with APIs, Python, and cloud technologies.
Experience developing stakeholder-facing analytics or AI solutions.
Strong communication and stakeholder management skills.

Source: Company Career page