Position Summary:

We are looking for a Senior Data Engineer to design, build, and scale the core data infrastructure that powers analytics, AI/ML, and Generative AI solutions across the organization. You will own end-to-end data pipeline development, drive architectural decisions, and ensure our data ecosystem is reliable, scalable, cost-efficient, and optimized for advanced AI use cases.

Key Responsibilities:

Core Engineering

• Architect, build, and optimize end-to-end data pipelines (batch + streaming) with a strong focus on performance, scalability, and cost efficiency.
• Design and maintain high-quality data models to support diverse analytical, ML, and GenAI workloads.
• Optimize SQL, Python, Spark/PySpark, and transformation workflows for performance at scale.
• Maintain and evolve data integrations across internal and external systems.
• Contribute to platform engineering, infrastructure automation, and the reliability of the data environment.

Architecture & Strategy

• Lead architecture discussions, influence technical direction, and propose scalable, future-proof solutions.
• Build data solutions that are innovative, modern, and avoid legacy approaches.
• Contribute to the development of self-service data layers enabling stakeholders across Product, Engineering, DS/ML, and Commercial teams.

GenAI & Advanced Use Cases

• Integrate Generative AI capabilities into data engineering workflows (data augmentation, enrichment, vectorization, embeddings, LLM integration, etc.).
• Build and maintain the data foundations required for AI/ML platforms (feature pipelines, real-time data flows, retrieval infrastructure).
• Collaborate with ML and GenAI teams to ensure data architecture supports model training, inference, and evaluation at scale.

Collaboration & Leadership

• Work closely with cross-functional teams (Data Science, Product, ML, DevOps, Analytics, Commercial) to deliver data solutions that drive business impact.
• Uphold high standards of data quality, governance, and reliability.
• Mentor other engineers and contribute to building a strong engineering culture.
• Communicate clearly and manage stakeholders to align on priorities and timelines.

Impact of the Role

As a Senior Data Engineer, you will shape the data backbone that powers strategic decision-making, AI initiatives, and next-generation product features. Your work will directly elevate the organization’s ability to build intelligent, GenAI-powered experiences and deliver scalable, high-impact data solutions.

Tech Stack

• Languages: Python, SQL
• Streaming: Kafka, Kinesis
• Data Stores: Redshift, ClickHouse, S3
• Orchestration: Dagster
• AWS: EKS/ECS, DMS, Glue, Athena, Lambda, Bedrock, Quick Suite, DynamoDB
• ETL/ELT: Fivetran, dbt
• IaC: Terraform + Terragrunt

Desired Qualifications:

• 7+ years as a Data Engineer, working with large-scale, production-grade data systems.
• Expert in SQL, Python, Spark/PySpark, and modern data warehousing techniques.
• Strong AWS experience, including serverless and scalable architectures.
• Proven experience designing performant, cost-efficient data pipelines (batch + streaming).
• Deep understanding of dimensional modeling, data warehouses, and industry best practices.
• Proficient with orchestration tools (Dagster, Airflow, Step Functions).
• Hands-on experience building automation and CI/CD workflows for data platforms.
• Practical experience applying Generative AI or LLM-based services to data workflows (e.g., data enrichment, classification, embedding pipelines, retrieval workflows).

Preferred Experience:

• Experience with Real Estate
• Familiarity with ETL tools: Fivetran, dbt, Airbyte.
• Experience with real-time analytics solutions (ClickHouse, Pinot, Rockset, Druid).
• Familiarity with BI tools (QuickSight, Looker, PowerBI, Tableau).
• Exposure to tagging/tracking tools (Snowplow, Tealium).
• Experience with Terraform & Terragrunt.
• Knowledge of GCP or Google Analytics is a plus.