Senior Data Engineer

Company Description

Join Sigma Software to build large-scale data infrastructure powering a real-time AdTech platform processing hundreds of millions of auction requests daily. We are looking for a Senior Data Engineer who enjoys solving complex distributed data challenges and building production-grade ML-oriented data systems.

You will become part of a dedicated Sigma Software team developing predictive modeling and optimization capabilities for a live advertising ecosystem. The role combines large-scale event processing, streaming and batch pipelines, experimentation infrastructure, and high-throughput data engineering in a cloud-native environment.

We as a company offer the opportunity to work on impactful global products, collaborate with experienced engineers, and contribute to architecture decisions while growing your expertise in large-scale distributed systems and modern data platforms.

CUSTOMER

Our Customer is a technology company operating supply-side infrastructure within the programmatic advertising ecosystem. The company manages a large-scale ad exchange handling hundreds of millions of auction requests per day and is actively investing in predictive decisioning technologies to optimize advertising outcomes in real time.

PROJECT

The project focuses on building a predictive modeling and optimization platform on top of a live ad exchange environment. The platform performs real-time supply scoring and filtering, contextual performance estimation, look-alike audience generation, and multi-objective optimization under business constraints.

The solution processes massive-scale event and auction datasets and includes feature engineering pipelines, streaming and batch ingestion, experimentation infrastructure, point-in-time-correct training data generation, and ML-oriented data services with strict operational reliability and compliance requirements.

Job Description

• Write and defend diagnostic SQL queries against large-scale production datasets
• Build and maintain ingestion pipelines for bid, win, and impression logs into BigQuery
• Harmonize fields across independently designed datasets and maintain versioned field mappings
• Develop point-in-time-correct feature tables and aggregation pipelines
• Design and maintain conversion and labeling pipelines with delayed label handling
• Own the data serving write path, schema contracts, publishing flows, and freshness SLOs
• Build experimentation infrastructure including traffic splitting and reporting pipelines
• Perform large-scale historical backfills and safe reprocessing after mapping changes
• Implement data isolation and safe-aggregation controls for advertiser data protection
• Develop automated data quality validation frameworks
• Collaborate closely with Customer engineers and prepare operational documentation
• Contribute to architecture discussions and platform scalability improvements

Qualifications

• 5+ years of experience in Data Engineering
• At least 2 years of experience working with production ML or large-scale analytics pipelines
• Expert-level SQL skills including window functions and incremental processing patterns
• Strong Python skills for production-grade pipeline development
• Hands-on experience with Spark or PySpark
• Experience designing ETL / ELT pipelines with Airflow, Cloud Composer, Dagster, or similar tools
• Experience working with cloud data warehouses at scale, preferably BigQuery
• Strong understanding of data modeling and point-in-time correctness
• Experience working with event-driven or clickstream datasets at very large scale
• Experience supporting business-critical production pipelines
• Upper-Intermediate English level or higher

WILL BE A PLUS

• Experience with GCP services including Dataflow, Pub/Sub, GCS, and Beam
• Experience building streaming or near-real-time ingestion systems
• Understanding of feature stores, train/serve skew, and label leakage prevention
• Experience in AdTech or auction-based environments
• Experience handling delayed or incomplete labels in ML systems
• Experience with dbt or similar transformation frameworks
• Experience delivering solutions into Customer-owned infrastructure
• Knowledge of GDPR/CCPA-related privacy engineering practices
• Experience with experimentation infrastructure and statistical validation pipelines
• Experience working in hybrid cloud/on-prem Linux environments
• Terraform and Kubernetes experience
• Experience optimizing warehouse cost and performance

Additional Information

PERSONAL PROFILE

• Strong analytical and problem-solving skills
• Ownership-oriented mindset
• Ability to work independently in a client-facing environment
• Strong communication and documentation skills
• Comfortable working in a fast-paced engineering environment
• Collaborative and proactive attitud