Data Engineer

In this role, you will play a crucial role in building and evolve backend services and scalable pipelines for batch and near-real-time data ingestion. You will design robust data models, optimize platform performance, and ensure cost efficiency within a GCP environment. In this role, you will champion engineering best practices across CI/CD, observability, and automation to power a reliable, high-performance data infrastructure.

Responsibilities

Architecture & Evolution

Collaborate on designing and improving the data platform’s core architecture, scalability, reliability, and performance.

Best Practices Champion

Drive engineering excellence by contributing to and implementing best practices in CI/CD, data observability, versioning, and automation.

Cost Optimization

Apply a cost-conscious mindset to cloud resource utilization (specifically within GCP) to ensure efficient data operations.

Pipeline & Model Management

Design, build, and maintain scalable data pipelines and models for both batch and near-real-time data processing.

Backend Engineering

Build and evolve backend services specifically tailored for data ingestion and analytics.

Platform Maintenance

Ensure the day-to-day stability, monitoring, and API-based connectivity of the data platform stack.

Collaborative Autonomy

Function effectively as an Individual Contributor (IC) who is comfortable working independently while maintaining close, proactive collaboration with peers.

Cross-Functional Alignment

Work alongside team members to align data ingestion and pipeline capabilities with broader business analytics needs.

Requirements

Years of Experience

3–4+ years of professional experience operating as an Individual Contributor (IC) Data Engineer.

Technical Foundations

Proven track record working with distributed systems, data modeling, observability frameworks, and API-based architectures.

Programming Language Proficiency

Strong, production-level proficiency in backend languages such as Python or Golang.

Modern Data Stack Expertise

• Deep knowledge of modern data tools and infrastructure, specifically:
• Orchestration & Streaming: Airflow, Pub/Sub
• Data Warehousing & Storage: BigQuery, ClickHouse
• Serverless/Compute: Cloud Run, Cloud Functions

Cloud Ecosystems

Strong conceptual and practical knowledge of cloud environments, with a heavy preference for Google Cloud Platform (GCP).