Data Engineer Data Pipelines and ETL

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Job

The Data Engineering team is hiring a Data Engineer – Data Pipeline & ETL. You will help build and maintain scalable data platforms and ETL/ELT pipelines in a fast-moving environment. In this role, you will build and support batch and real-time data systems powering analytics, ML, and AI applications. You will also grow your expertise in modern data architecture and cloud-native best practices.

• Design, develop, and maintain scalable batch and streaming data pipelines for large-scale structured and unstructured datasets.
• Build robust ETL/ELT frameworks supporting analytics, BI, experimentation, and machine learning use cases.
• Optimize pipelines for performance, reliability, scalability, and cost efficiency.
• Implement advanced ingestion patterns including CDC, incremental loads, and event-driven processing.
• Design scalable, dimensional, and hybrid data models optimized for analytics and ML use cases.
• Develop reusable transformation layers (semantic layers) that serve BI, ML, and AI applications.
• Write optimized, production-grade SQL for large-scale analytics workloads.
• Contribute to query optimization, indexing, partitioning, and performance tuning across distributed systems and cloud warehouses.
• Build and maintain modular data components following established framework patterns.
• Contribute to architectural decisions across streaming systems, data lakes, and warehouses.
• Implement automated data validation, anomaly detection, and monitoring frameworks.
• Establish data lineage and metadata standards to support reproducibility in ML workflows.
• Enforce governance, privacy, and security best practices, particularly for sensitive AI datasets.
• Ensure responsible AI data usage and compliance standards.

• 2–4+ years of experience building and scaling ETL/ELT pipelines in production environments.
• Experience with workflow orchestration tools such as Airflow, Composer, or similar platforms.
• Strong understanding of distributed data processing concepts.

• Expert-level SQL skills for large-scale transformation and analytics.
• Experience designing scalable warehouse schemas and ML-ready data layers.
• Strong experience optimizing complex queries across multi-terabyte datasets.

• Proficiency in Python (or similar language) for data processing and ML pipeline integration.
• Experience with distributed processing frameworks such as Spark.
• Familiarity integrating data pipelines with ML platforms such as Vertex AI (preferred), Databricks ML, or equivalent.

• Experience building real-time data pipelines using Kafka, Pub/Sub, or similar technologies.
• Understanding of feature streaming, low-latency data processing, and event-driven architectures.
• Ability to architect and build real-time dashboards using Superset.

• Experience designing cloud-native data architectures (GCP preferred).
• Experience with Lakehouse architectures and cloud data warehouses.
• Familiarity with vector databases, embeddings pipelines, and AI-serving infrastructure is a plus.

• Bachelor's or Master's degree in Computer Science, Engineering, or related field (or equivalent experience).
• 2–4+ years of experience in data engineering, data pipeline development, or related fields.
• Strong foundation in modern data engineering principles, distributed systems design, and cloud-native architectures.
• Demonstrated ability to design and operate large-scale production data systems.
• Proven track record…