GCP Data Platform Engineer maintaining and optimizing production data platforms for Innodata, a global data engineering and AI services company. Supporting Airflow pipelines, GCP infrastructure, reliability, and troubleshooting.
About the role
- Senior Data Engineer building tools and infrastructure to support Data Products. Collaborating with cross-functional teams to develop data solutions and scale data infrastructure.
Responsibilities
- You’re a builder. You will design, build, and deploy components of a modern data platform, including CDC-based ingestion using Debezium and Kafka, a centralized Hudi-based data lake, and a mix of batch, incremental, and streaming data pipelines.
- You ensure continuity while driving modernization. You will maintain and enhance the existing Amazon Redshift data warehouse and legacy Python ELT pipelines, ensuring stability and reliability, while accelerating the transition to a brand-new Databricks-based analytics and processing environment.
- You balance innovation with operational excellence. You enjoy building fault-tolerant, scalable, and cost-efficient data systems, and you continuously improve observability, performance, and reliability across both legacy and modern platforms.
- You collaborate to deliver impact. You will work closely with cross-functional partners to plan and roll out data infrastructure and processing pipelines that support analytics, machine learning, and GenAI use cases. You enjoy enabling teams across Wave by ensuring data and insights are delivered accurately and on time.
- You thrive in ambiguity and take ownership. You are self-motivated and comfortable working autonomously, identifying opportunities to optimize pipelines and improve data workflows, even under tight timelines and evolving requirements.
- You keep the platform reliable. You will respond to PagerDuty alerts, troubleshoot incidents, and proactively implement monitoring and alerting to minimize incidents and maintain high availability.
- You’re a strong communicator. Colleagues rely on you for technical guidance. Your ability to clearly explain complex concepts and actively listen helps build trust and resolve issues efficiently.
- You’re customer-minded. You will assess existing systems, improve data accessibility, and deliver practical solutions that enable internal teams to generate actionable insights and enhance our external customers' experience.
Requirements
- Data Engineering Expertise: Bring 6+ years of experience in building data pipelines and managing a secure, modern data stack. This includes CDC streaming ingestion using tools like Debezium into a data warehouse that supports AI/ML workloads.
- AWS Cloud Proficiency: At least 3 years of experience working with AWS cloud infrastructure, including Kafka (MSK), Spark / AWS Glue, and infrastructure as code (IaC) using Terraform.
- Data modelling and SQL: Fluency in SQL, strong understanding of data modelling principles and data storage structures for both OLTP and OLAP.
- Databricks experience: Experience developing or maintaining a production data system on Databricks.
- Strong Coding Skills: Write and review high-quality, maintainable code that enhances the reliability and scalability of our data platform. We use Python, SQL, and dbt extensively, and you should be comfortable leveraging third-party frameworks to accelerate development.
- Data Lake Development: Prior experience building data lakes on S3 using Apache Hudi with Parquet, Avro, JSON, and CSV file formats.
- CI/CD Best Practices: Experience developing and deploying data pipeline solutions using CI/CD best practices to ensure reliability and scalability.
Benefits
- Bonus Structure
- Employer-paid Benefits Plan
- Health & Wellness Flex Account
- Professional Development Account
- Wellness Days
- Holiday Shutdown
- Wave Days (extra vacation days in the summer)
- Get A-Wave Program (work from anywhere in the world up to 90 days)
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