Staff / Senior Data Engineer

At HG Insights we provides AI-powered revenue growth intelligence and technology intelligence data to help B2B companies refine their go-to-market (GTM) strategies by analyzing market size, identifying potential customers, and uncovering in-market purchasing signals. With the recent acquisitions of MadKudu and TrustRadius, we’ve created an agentic GTM ecosystem that eliminates manual handoffs, guesswork, and siloed signals.

We’re searching for a strong Data Engineer to take this ecosystem to the next level. You’ll play a critical role in tackling high-impact projects, including large-scale data integration, improving and enriching intent signals, and building the foundational capabilities that will power our future AI initiatives. This is an opportunity to work with complex data pipelines at scale, shape the backbone of our intelligence platform, and directly influence how thousands of enterprise customers uncover growth opportunities.

What You Will Do

Build and maintain ETL pipelines using Apache Airflow in our kubernetes environment, processing 10M+ daily events from MongoDB to PostgreSQL and BigQuery

Design real-time data streaming architectures using Google Cloud Pub / Sub for visitor tracking, intent signals, and company resolution workflows

Develop ML data infrastructure for AI-powered features including vector embeddings, semantic search, and automated content generation using OpenAI and Google Vertex AI

Create analytics and reporting systems on BigQuery for customer intent data, lead scoring models, and business intelligence dashboards

Implement data quality and monitoring frameworks ensuring accuracy across the entire data pipeline from source to customer delivery

Build customer data delivery systems including Snowflake integration and API endpoints for enterprise data consumption

What You Will Be Responsible For

Data pipeline reliability with 99.9% uptime for critical business intelligence feeding customer-facing intent data products

Data accuracy and consistency across MongoDB → PostgreSQL → BigQuery → Snowflake transformation chains

ML infrastructure performance supporting real-time vector search, embedding generation, and AI model inference

Scalable data architecture handling exponential growth in visitor data and intent signals

Customer data SLAs ensuring timely delivery of intent data to enterprise customers with contractual requirements

Building Dashboards and alerting for management of these systems

What You Will Need

9+ years data engineering with Python, SQL, and distributed data processing frameworks

Strong ETL / ELT experience with Apache Airflow, dbt, or similar orchestration tools

Cloud data platform expertise with BigQuery, Cloud Storage, and data warehouse optimization

Real-time streaming experience with Pub / Sub, Kafka, or similar event-driven systems

Database proficiency across PostgreSQL, MongoDB, and modern data warehouse technologies

Nice to Have

ML / AI pipeline experience with vector databases (Pinecone), embedding models, and LLM integration

Intent data and lead scoring domain knowledge in B2B marketing technology

Apache Beam or Dataflow for large-scale data processing

Customer analytics and behavioral data modeling experience