Senior Data Engineer

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.

Requirements

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

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 attitude

Responsibilities

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