Senior Data Engineer

What you will do

Data Engineering & Platform Development

• Design,

  develop, and maintain scalable batch and real-time data pipelines.

• Build and

  optimize ETL/ELT workflows using modern data engineering frameworks.

• Develop

  and manage data lake, data warehouse, lake house architecture and snowflakes.

• Implement

  data quality checks, validation frameworks, and monitoring solutions.

• Optimize

  data processing jobs for performance, scalability, and cost efficiency.

• Collaborate

  with analytics, BI, and AI teams to deliver trusted datasets.

Cloud & Infrastructure

• Build and

  maintain cloud-native data platforms on AWS, Azure, or GCP.

• Implement

  Infrastructure as Code (IaC) using Terraform or equivalent tools.

• Support

  CI/CD implementation for data engineering workloads.

• Ensure

  security, compliance, and governance standards across data platforms.

Collaboration & Delivery

• Work

  directly with stakeholders to understand business requirements and translate

  them into technical solutions.

• Participate

  in architecture discussions, design reviews, and sprint planning.

• Perform

  code reviews and mentor junior engineers.

• Contribute

  to technical documentation, standards, and reusable assets.

Innovation & Practice Building

• Evaluate

  and recommend modern data engineering tools and frameworks.

• Contribute

  to internal accelerators, reusable components, and best practices.

• Support

  proof-of-concepts and emerging technology initiatives in analytics, AI, and

  data platforms.

Requirements

Must have

• 4 to 5

  years of experience in Data Engineering.

• Strong

  expertise in Python, PySpark, and SQL.

• Hands-on

  experience with Spark-based data processing at production scale.

• Experience

  with cloud platforms such as AWS, Azure, or GCP.

• Strong

  understanding of Data Lakes, Data Warehouses, and Lakehouse architectures.

• Experience

  with orchestration tools such as Airflow, Azure Data Factory, or similar.

• Experience

  with data modeling, performance tuning, and pipeline optimization.

• Knowledge

  of CI/CD, Git, and DevOps practices.

• Strong

  communication and stakeholder management skills

Mandatory

Certifications (Any One Required)

• Databricks

  Certified Data Engineer Associate

• Databricks

  Certified Data Engineer Professional

• AWS

  Certified Data Engineer - Associate

• Microsoft

  Certified: Azure Data Engineer Associate (DP-203)

• Google Professional

  Data Engine

Benefits

• Opportunity to work on enterprise-scale cloud

and data engineering projects.

• Exposure to global clients and modern data

platforms.

• Sponsorship for advanced cloud and data

engineering certifications.

• Clear career progression toward Lead Data

Engineer and Architect roles.

• Collaborative engineering culture focused on

learning and innovation.

• Competitive

compensation with performance-based incentives.