Lead Data Engineer

Mobikwik; Gurugram District; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

Mobikwik is seeking a senior Data Engineer to build scalable data platforms on AWS and design end-to-end data pipelines from diverse sources. You will model data for analytics, optimize processing for performance and cost, and collaborate with data scientists, product managers, and analysts to enable experimentation and insights.

The role requires 6–9 years of experience with PySpark, SQL, Python, and AWS services, plus hands-on mentoring skills.

Qualifications

• Bachelor’s/Master’s in CS/Engineering or related field.
• 6–9 years of Data Engineering experience with hands-on data platforms.
• Strong Python, PySpark, SQL, and shell scripting skills.
• Expertise in AWS data stack: Glue, Redshift, Athena, EMR, S3.
• Experience with streaming: Kafka, Kinesis.
• ETL/ELT workflows and orchestration: Airflow, Step Functions.
• Databases: MySQL, MongoDB, Hive; NoSQL concepts.
• Ability to mentor and lead while coding hands-on.

Responsibilities

• Build and maintain scalable data platforms on AWS.
• Design and implement ETL/ELT pipelines from diverse sources.
• Develop data models and warehousing schemas for analytics.
• Tune and optimize data processing for performance and cost.
• Collaborate with data scientists, product managers, and analysts.
• Implement data quality, monitoring, and alerting frameworks.
• Mentor junior engineers and drive architectural discussions.

Skills

Python

Pyspark

SQL

Shell scripting

Data modeling

Problem solving

Mentorship

Education

Bachelors or Masters degree in Computer Science, Engineering, or related field

Tools

AWS Glue

Amazon Redshift

Amazon Athena

Amazon EMR

Amazon S3

Kafka

Kinesis

Airflow

Step Functions

MySQL

MongoDB

Hive

Job description

Role & responsibilities

1.Data Infrastructure: Build and maintain scalable, secure, and high-performance data platforms on AWS (Glue, Redshift, S3, EMR, Athena). Ensure systems are reliable, cost-efficient, and meet business

SLAs.

2. Data Pipelines: Design and implement ETL/ELT workflows to ingest, process, and transform large structured and unstructured datasets from multiple sources (transactional DBs, APIs, logs, Kafka).

Enable both batch and real-time data streaming solutions.

3. Data Modeling & Warehousing: Develop efficient data models and schemas to support analytics, BI, and reporting.

Optimize storage and queries in Redshift, Hive, or MySQL.

4. Performance & Optimization: Continuously tune data processing frameworks (Pyspark, Glue, Kafka, Hive) to ensure performance, scalability, and cost-effectiveness.

5. Collaboration: Partner with data scientists, product managers, and analysts to translate requirements into scalable data solutions. Ensure data availability and accessibility for experimentation, insights,

and decision-making.

6. Quality & Monitoring: Implement robust monitoring, alerting, and data validation frameworks to ensure data integrity, reliability, and availability.

7. Team Mentorship: Guide junior engineers with technical reviews, coding best practices, and architecture discussions. Foster a culture of innovation, ownership, and continuous improvement.

Preferred candidate profile

Bachelors or Masters degree in Computer Science, Engineering, or related field.

6–9 years of experience in Data Engineering, with hands‑on expertise in:

• Python, Pyspark, SQL, Shell scripting
• AWS data stack (Glue, Redshift, Athena, EMR, S3)
• Streaming technologies (Kafka, Kinesis)
• ETL/ELT frameworks and data orchestration tools (Airflow, Step Functions).
• Strong understanding of databases (MySQL, MongoDB, Hive, NoSQL) and query optimization.
• Proven experience designing and scaling data pipelines for large volumes of data.
• Excellent problem-solving, debugging, and performance‑tuning skills.
• Ability to mentor, influence, and lead while remaining hands‑on with code.