Data Engineer

We are looking for a hands-on Data Engineer to help build and scale our modern data platform. In this role, you will work closely with engineering, product, and analytics teams to develop reliable, high-performance data pipelines and systems.

You’ll contribute to both batch and real-time data processing using technologies like Databricks, AWS, and Kafka, while helping ensure data quality, accessibility, and usability across the organization. You’ll play a key role in enabling data activation, ensuring that high-quality data flows not only into the warehouse but also outward to business tools like Salesforce, HubSpot, and Braze. Additionally, you will help power next-generation AI-driven applications, including agent-based systems and retrieval-augmented generation (RAG), by building robust data foundations and pipelines. This is a great opportunity for someone who enjoys solving data challenges end-to-end from ingestion to insights.

Responsibilities

• Build and maintain scalable data pipelines (batch and streaming) using tools like Databricks, Spark, Kafka, and AWS services
• Design, develop, and optimize ETL/ELT workflows using DBT, PySpark, SQL, and tools like Fivetran
• Partner closely with marketing and growth teams to enable data use cases such as segmentation, campaign targeting, and lifecycle analytics
• Develop and maintain reverse ETL pipelines to sync data from the warehouse to tools like Salesforce, HubSpot, Braze, and other downstream systems
• Create and manage curated datasets to support analytics, reporting, and go-to-market initiatives
• Build and maintain dashboards and reporting layers to support marketing and business performance tracking
• Support AI/ML and agent-based applications by preparing and serving high-quality datasets for RAG pipelines and MCP (Model Context Protocol) integrations
• Monitor pipeline performance, troubleshoot issues, and ensure high data reliability and quality
• Implement data quality checks, validations, and alerting mechanisms across both ingestion and activation layers
• Collaborate with cross-functional teams to define data contracts and ensure consistency across systems

• 6+ years of experience in data engineering or a related field
• Strong hands-on experience with Databricks, AWS (S3, Glue, Athena, EMR, etc.), and Kafka
• Proficiency in Python (PySpark) and SQL for large-scale data processing
• Experience building and maintaining ETL/ELT pipelines (DBT/Airflow or similar experience preferred)
• Experience with data ingestion tools such as Fivetran (or similar)
• Familiarity with reverse ETL / data activation workflows and syncing data to tools like Salesforce, HubSpot, Braze
• Exposure to or experience with AI/ML data pipelines, including RAG architectures, vector databases, or embeddings workflows
• Familiarity with agent-based systems, MCP integrations, or LLM-powered applications is a strong plus
• Experience working with marketing, Product or growth teams on data use cases (segmentation, attribution, campaign analytics, etc.)
• Understanding of data modeling and working with large-scale datasets (batch and streaming)
• Experience creating dashboards and supporting reporting workflows (BI tools) for both internal and external audiences
• Strong problem-solving skills and ability to debug production data issues
• Strong communication skills and ability to work collaboratively across teams
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