Unlock Your Potential with Senior and Lead Data Engineer Roles in Bangalore
Are you ready to advance your career in data engineering? We’re hiring Senior Data Engineers and Lead Data Engineers for exciting opportunities in Bangalore. If you’re passionate about working with cutting-edge technologies, have expertise in Python, SQL, Adobe RTCDP, and cloud platforms like AWS, Azure, or GCP, this could be the role for you!
🛠️ About the Job
We are looking for seasoned professionals with a proven track record in data engineering, eager to solve complex challenges in a fast-paced environment.
Position Details:
1. Senior Data Engineer
- Experience Required: 5 to 9 years
- Skills Required: Python, SQL, Adobe RTCDP (Real-Time CDP), AWS/Azure/GCP
2. Lead Data Engineer
- Experience Required: 8 to 13 years
- Skills Required: Python, SQL, Adobe RTCDP (Real-Time CDP), AWS/Azure/GCP
📍 Location: Bangalore
- Final Round: In-person (Face-to-Face) interview in Bangalore. Only apply if you’re available for F2F interviews.
💼 Key Responsibilities
- Build and optimize data pipelines, ensuring efficient handling of both structured and unstructured data.
- Develop robust data models and data catalogs, enabling seamless data management and analytics.
- Design scalable solutions using cloud platforms (AWS, Azure, GCP) and implement advanced data engineering practices.
- Work extensively with Adobe Real-Time Customer Data Platform (RTCDP) for customer data management.
- Collaborate with cross-functional teams in an Agile environment to deliver data-driven solutions.
- Write and optimize complex SQL queries and scripts to enable insightful analytics.
- Utilize programming knowledge (e.g., JavaScript) for automating workflows and enhancing platform capabilities.
🎯 Mandatory Skills & Requirements
- Strong Python Proficiency: Hands-on experience in data engineering projects is essential.
- SQL Mastery: Ability to write and debug complex queries effectively.
- Adobe RTCDP Expertise: Prior experience in Adobe Real-Time CDP is a must.
- Cloud Expertise: Exposure to AWS, Azure is mandatory, while GCP experience is a plus.
- Data Modeling & Cataloging: Strong understanding of data modeling concepts and tools.
- Agile Methodology: Experience in Agile project management processes.
- AdTech/MarTech Experience: Preferred but not mandatory.
- Excellent Communication: Strong written and verbal skills to collaborate effectively across teams.
🚀 Why Join?
- Work with cutting-edge tools and technologies in the rapidly evolving data engineering landscape.
- Join a high-performing team dedicated to creating impactful data solutions.
- Contribute to innovative projects in domains like AdTech and MarTech.
- Competitive compensation and growth opportunities tailored to your expertise.
- Be part of an Agile, collaborative, and dynamic work environment.
📢 Who Should Apply?
This role is ideal for data professionals with a passion for innovation and problem-solving. If you meet the criteria, have relevant experience, and are available for an in-person interview in Bangalore, we encourage you to apply!
📌 How to Apply?
Send your updated resume along with a brief note about your experience with Adobe RTCDP and cloud platforms to contact@aemskills.com for getting apply link and reference for this job in next 7 days.
💡 Boost Your Career with Us
Join us and take the next step in your data engineering journey. Whether you’re a Senior Data Engineer with 5+ years of experience or a Lead Data Engineer with 8+ years, this is your opportunity to shine.
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Here are 10 expected interview questions and scenarios based on the job opening for Senior Data Engineer and Lead Data Engineer:
1. Python Proficiency
Scenario: You are tasked with processing large datasets for real-time analytics.
- Question: How would you use Python to design an ETL pipeline to ingest, transform, and load structured and unstructured data?
- Follow-up: Can you demonstrate using libraries like Pandas or PySpark for these operations?
2. SQL Mastery
Scenario: A team member has written an inefficient SQL query that impacts database performance.
- Question: How would you optimize a complex SQL query to reduce execution time and improve efficiency?
- Follow-up: Can you explain window functions or CTEs (Common Table Expressions) and when you would use them?
3. Adobe RTCDP Expertise
Scenario: You need to integrate Adobe Real-Time Customer Data Platform with an existing CRM to manage customer profiles dynamically.
- Question: How would you design a data flow to synchronize customer profiles between Adobe RTCDP and a CRM system in real time?
- Follow-up: Can you share your experience with audience segmentation or data activation using RTCDP?
4. Cloud Platforms (AWS, Azure, GCP)
Scenario: You’re migrating a legacy on-premises data warehouse to the cloud.
- Question: How would you choose between AWS, Azure, and GCP for this project? What factors would you consider?
- Follow-up: Describe how you’ve implemented serverless data pipelines or used cloud-native tools like AWS Glue, Azure Data Factory, or BigQuery.
5. Data Modeling and Cataloging
Scenario: You are tasked with designing a scalable data model for a customer feedback analytics system.
- Question: How do you approach data modeling to ensure it accommodates both structured and unstructured data?
- Follow-up: Can you explain your experience with tools like Apache Atlas or Azure Purview for data cataloging?
6. Handling Large Data Sets
Scenario: You are processing billions of rows of transactional data for a real-time recommendation engine.
- Question: What techniques or tools would you use to efficiently handle and analyze such large datasets?
- Follow-up: Have you used distributed processing frameworks like Apache Spark or Dask in your projects?
7. Agile Methodology
Scenario: The team needs to deliver an MVP for a data pipeline within a tight deadline.
- Question: How do you plan sprints and manage team tasks using Agile methodology in a data engineering project?
- Follow-up: How do you handle changing requirements during an Agile sprint?
8. Automation and Infrastructure as Code (IaC)
Scenario: You need to automate the deployment of a data pipeline on AWS.
- Question: How would you implement Infrastructure as Code (IaC) for a data pipeline using tools like Terraform or CloudFormation?
- Follow-up: Can you explain a scenario where automation significantly improved your project’s delivery time?
9. Troubleshooting and Debugging
Scenario: One of your ETL pipelines fails intermittently during peak hours.
- Question: How would you debug and resolve the issue?
- Follow-up: Can you describe a situation where you resolved a critical data pipeline failure?
10. AdTech/MarTech Experience (Optional but Preferred)
Scenario: A client wants to use customer behavior data for targeted marketing campaigns.
- Question: How would you build a data pipeline to collect, process, and activate customer behavior data for AdTech or MarTech platforms?
- Follow-up: Can you share your experience in handling real-time analytics in a similar domain?
These questions are designed to test not only technical expertise but also problem-solving skills, project experience, and the ability to work with advanced tools and methodologies. Preparing real-life examples and scenarios from your past projects will help you ace the interview.
Cybersecurity Architect | Cloud-Native Defense | AI/ML Security | DevSecOps
With over 23 years of experience in cybersecurity, I specialize in building resilient, zero-trust digital ecosystems across multi-cloud (AWS, Azure, GCP) and Kubernetes (EKS, AKS, GKE) environments. My journey began in network security—firewalls, IDS/IPS—and expanded into Linux/Windows hardening, IAM, and DevSecOps automation using Terraform, GitLab CI/CD, and policy-as-code tools like OPA and Checkov.
Today, my focus is on securing AI/ML adoption through MLSecOps, protecting models from adversarial attacks with tools like Robust Intelligence and Microsoft Counterfit. I integrate AISecOps for threat detection (Darktrace, Microsoft Security Copilot) and automate incident response with forensics-driven workflows (Elastic SIEM, TheHive).
Whether it’s hardening cloud-native stacks, embedding security into CI/CD pipelines, or safeguarding AI systems, I bridge the gap between security and innovation—ensuring defense scales with speed.
Let’s connect and discuss the future of secure, intelligent infrastructure.