Senior Data Engineer

Data Direct Networks•New York, NY

About The Position

This is an incredible opportunity to be part of a company that has been at the forefront of AI and high-performance data storage innovation for over two decades. DataDirect Networks (DDN) is a global market leader renowned for powering many of the world's most demanding AI data centers, in industries ranging from life sciences and healthcare to financial services, autonomous cars, Government, academia, research and manufacturing. "DDN's A3I solutions are transforming the landscape of AI infrastructure." – IDC “The real differentiator is DDN. I never hesitate to recommend DDN. DDN is the de facto name for AI Storage in high performance environments” - Marc Hamilton, VP, Solutions Architecture & Engineering | NVIDIA DDN is the global leader in AI and multi-cloud data management at scale. Our cutting-edge data intelligence platform is designed to accelerate AI workloads, enabling organizations to extract maximum value from their data. With a proven track record of performance, reliability, and scalability, DDN empowers businesses to tackle the most challenging AI and data-intensive workloads with confidence. Our success is driven by our unwavering commitment to innovation, customer-centricity, and a team of passionate professionals who bring their expertise and dedication to every project. This is a chance to make a significant impact at a company that is shaping the future of AI and data management. Our commitment to innovation, customer success, and market leadership makes this an exciting and rewarding role for a driven professional looking to make a lasting impact in the world of AI and data storage. We’re looking for a Senior Data Engineer to own and evolve the infrastructure and data ingestion layer that powers DDN’s enterprise data platform. You’ll be responsible for the systems that move data from source to warehouse and the platform reliability that analytics engineers and business stakeholders depend on — not the modeling or dashboarding itself, but everything underneath it. You’ll work closely with analytics engineers, data analysts, and business stakeholders across Sales, Finance, Product, and other areas.

Requirements

5+ years in data engineering or platform/infrastructure engineering roles
Strong hands-on experience with GCP especially BigQuery, Cloud Composer (managed Airflow), IAM
Proficiency in Terraform (or equivalent IaC) for managing cloud resources across environments
Strong Python skills with demonstrated ability to build integrations, automation, and data pipelines
Expert SQL and understanding of data warehouse patterns (schemas, partitioning, access controls)
Experience with CI/CD for data infrastructure
Experience integrating data from diverse sources including SaaS platforms, on-prem systems, and cloud-hosted product infrastructure using both managed tools (e.g. Fivetran) and custom pipelines
Experience with real-time / streaming data with demonstrated experience building and operating event-driven pipelines (Pub/Sub, Kafka, Dataflow, or similar)
Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience

Nice To Haves

Experience with Sigma Computing or similar BI tool administration/API integration
Familiarity with data quality frameworks (e.g., Elementary, Great Expectations)
Experience with data governance tooling — access controls, column-level security, policy enforcement
Familiarity with dbt from a platform perspective — how it runs, how it’s deployed, what it needs from infrastructure
Prior experience in a small data team where you wore multiple hats

Responsibilities

Data ingestion — manage batch ingestion tools (Fivetran), build custom pipelines where needed, and design and implement real-time/streaming ingestion for product telemetry and event data
Infrastructure & architecture — manage and extend our GCP platform (BigQuery, Composer, IAM, networking) using Terraform, shape the technical direction of the platform as needs evolve, and maintain CI/CD pipelines for safe deployments across repos
Platform reliability — keep production systems healthy, monitor pipeline quality, investigate failures, and reduce technical debt
Collaboration — partner with analytics engineers on dbt and BI platform infrastructure needs; drive technical conversations about provisioning and scaling strategy

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