On-Site
Full-Time
Kochi, Kerala
India
About the Role
We are seeking a results-driven Senior Data Engineer for our German client to lead the development and implementation of advanced data infrastructure with a strong focus on graph databases (Neo4j) and cloud-based architecture (AWS, GCP, Azure). In this role, you will be responsible for transforming complex data systems into scalable, secure, and high-performing platforms to power next-generation analytics. This is an opportunity to drive meaningful outcomes while building expertise in a fast-moving, DevOps-oriented environment. Career growth opportunities include technical leadership and potential progression into data architecture or platform engineering leadership roles.
Job Responsibilities
Lead the design and implementation of a scalable graph data architecture leveraging Neo4j, ensuring optimal query performance and alignment with business analytics needs within the first 90 days.
Own the full lifecycle of data ingestion and ETL pipelines, including development, orchestration, monitoring, and optimization of data flows across multiple cloud environments, with measurable reduction in latency and improved data quality within 6 months.
Deploy and maintain infrastructure using Terraform, supporting infrastructure-as-code best practices across AWS and GCP, ensuring automated, reproducible deployments and >99.9% system uptime.
Automate CI/CD workflows using GitHub Actions, reducing manual operations by 50% within the first 6 months through effective automation of test, deploy, and monitoring pipelines.
Collaborate with cross-functional engineering, data science, and security teams to integrate and support graph-based solutions in production with clear documentation, support structures, and knowledge sharing.
Establish and enforce data security and access protocols, working with DevOps and InfoSec to ensure compliance with organizational standards by month 9.
Desired Qualifications
Demonstrated success designing and maintaining production-grade graph database systems (preferably Neo4j).
Strong command of Cypher, SQL, and Python for scalable data engineering solutions.
Hands-on experience with Terraform, including managing multi-cloud infrastructure.
Proven capability in building robust, reliable ETL pipelines and orchestrating workflows.
Experience with CI/CD tools like GitHub Actions in a production DevOps environment.
Strong communication skills, able to explain complex data concepts clearly across teams.
Ability to independently identify and implement performance optimizations in data systems.
Job Responsibilities
Lead the design and implementation of a scalable graph data architecture leveraging Neo4j, ensuring optimal query performance and alignment with business analytics needs within the first 90 days.
Own the full lifecycle of data ingestion and ETL pipelines, including development, orchestration, monitoring, and optimization of data flows across multiple cloud environments, with measurable reduction in latency and improved data quality within 6 months.
Deploy and maintain infrastructure using Terraform, supporting infrastructure-as-code best practices across AWS and GCP, ensuring automated, reproducible deployments and >99.9% system uptime.
Automate CI/CD workflows using GitHub Actions, reducing manual operations by 50% within the first 6 months through effective automation of test, deploy, and monitoring pipelines.
Collaborate with cross-functional engineering, data science, and security teams to integrate and support graph-based solutions in production with clear documentation, support structures, and knowledge sharing.
Establish and enforce data security and access protocols, working with DevOps and InfoSec to ensure compliance with organizational standards by month 9.
Desired Qualifications
Demonstrated success designing and maintaining production-grade graph database systems (preferably Neo4j).
Strong command of Cypher, SQL, and Python for scalable data engineering solutions.
Hands-on experience with Terraform, including managing multi-cloud infrastructure.
Proven capability in building robust, reliable ETL pipelines and orchestrating workflows.
Experience with CI/CD tools like GitHub Actions in a production DevOps environment.
Strong communication skills, able to explain complex data concepts clearly across teams.
Ability to independently identify and implement performance optimizations in data systems.