Senior Data Engineer

Overview

Job Summary

As a Data Engineer, you will be instrumental in designing, building, and maintaining the scalable data infrastructure that powers our AI-driven platform. You will work closely with data scientists, software engineers, and business stakeholders to ensure seamless data flow, quality, and availability to enable advanced analytics and operational intelligence.

Key Responsibilities:

1. Data Architecture & Pipeline Development

Design and implement scalable and resilient data pipelines to ingest, process, and

transform large volumes of structured and unstructured data from various sources.

2. ETL/ELT Workflows

Build and optimize robust ETL/ELT processes to ensure clean and consistent data

availability for analytics and machine learning models.

3. Data Warehousing

Develop and maintain efficient data models and storage solutions using modern data

warehouses (e.g., Snowflake, BigQuery, Redshift).

4. Collaboration with Cross-functional Teams

Partner with data scientists, software developers, and product teams to understand data

requirements and deliver effective solutions.

5. Data Quality & Governance

Implement data validation, monitoring, and quality checks. Ensure data consistency,

lineage, and adherence to governance standards.

6. Performance Optimization

Tune data workflows and query performance for large-scale data processing and

real-time analytics.

7. Tooling and Automation

Automate repetitive data engineering tasks and improve operational efficiency using

orchestration tools like Airflow or Prefect.

8. Documentation

Create and maintain detailed technical documentation including data dictionaries, flow

diagrams, and operational guides.

9. Security and Compliance

Implement best practices to secure data access and ensure compliance with data privacy

regulations.

Requirements:

● Education: Bachelor’s or Master’s degree in Computer Science, Data Engineering, or

related field.

● Programming Proficiency: Strong experience in Python or Scala, and SQL.

● Data Pipeline Tools: Proficiency in data pipeline frameworks such as Apache Spark,

Kafka, or Flink.

● Database Experience: Experience with both relational (PostgreSQL, MySQL) and NoSQL

databases (MongoDB, Cassandra).

● Cloud Expertise: Hands-on experience with cloud platforms (AWS, GCP, or Azure) and

cloud-native data tools.

● Analytical Thinking: Ability to solve complex data challenges and work with ambiguity in

datasets.

● Communication: Strong communication skills to work with both technical and

non-technical stakeholders.

● Version Control & CI/CD: Familiarity with Git, CI/CD tools, and DevOps for data pipelines.

Skills and Abilities:

● Experience with machine learning data pipelines and feature stores

● Knowledge of data lake architecture and technologies like Delta Lake or Apache Hudi

● Familiarity with Kubernetes, Docker, and infrastructure-as-code tools

● Prior experience working in an agile or scrum-based environment

What We Offer:

● Competitive salary and benefits package.

● Opportunities for professional development and career growth.

● A supportive and collaborative work environment.

● Access to the latest technologies and tools.

Work Environment

The Data Engineer will be part of a dynamic and agile environment, collaborating with a

high-performing cross-functional team to deliver scalable and intelligent data solutions.

Job Type: Full-time

Pay: ₹600,000.00 - ₹900,000.00 per year

Schedule:

• Day shift

Work Location: In person

Application Deadline: 18/04/2025