Position Summary:

We are looking for a Data Engineer to build reliable, scalable data pipelines and contribute to the core data ecosystem that powers analytics, AI/ML, and emerging Generative AI use cases. You will work closely with senior engineers and data scientists to deliver high-quality pipelines, models, and integrations that support business growth and internal AI initiatives.

 

Key Responsibilities

Core Engineering

• Build and maintain batch and streaming data pipelines with strong emphasis on reliability, performance, and efficient cost usage.
• Develop SQL, Python, and Spark/PySpark transformations to support analytics, reporting, and ML workloads.
• Contribute to data model design and ensure datasets adhere to high standards of quality, structure, and governance.
• Support integrations with internal and external systems, ensuring accuracy and resilience of data flows.

GenAI & Advanced Data Use Cases

• Build and maintain data flows that support GenAI workloads (e.g., embedding generation, vector pipelines, data preparation for LLM training and inference).
• Collaborate with ML/GenAI teams to enable high-quality training and inference datasets.
• Contribute to the development of retrieval pipelines, enrichment workflows, or AI-powered data quality checks.

Collaboration & Delivery

• Work with Data Science, Analytics, Product, and Engineering teams to translate data requirements into reliable solutions.
• Participate in design reviews and provide input toward scalable and maintainable engineering practices.
• Uphold strong data quality, testing, and documentation standards.
• Support deployments, troubleshooting, and operational stability of the pipelines you own.

Professional Growth & Team Contribution

• Demonstrate ownership of well-scoped components of the data platform.
• Share knowledge with peers and contribute to team learning through code reviews, documentation, and pairing.
• Show strong execution skills — delivering high-quality work, on time, with clarity and reliability.

Impact of the Role

In this role, you will help extend and strengthen the data foundation that powers analytics, AI/ML, and GenAI initiatives across the company. Your contributions will improve data availability, tooling, and performance, enabling teams to build intelligent, data-driven experiences.

Tech Stack

• Languages: Python, SQL, Java/Scala
• Streaming: Kafka, Kinesis
• Data Stores: Redshift, Snowflake, ClickHouse, S3
• Orchestration: Dagster (Airflow legacy)
• Platforms: Docker, Kubernetes
• AWS: DMS, Glue, Athena, ECS/EKS, S3, Kinesis
• ETL/ELT: Fivetran, dbt
• IaC: Terraform + Terragrunt

 

Desired Qualifications:

• 5+ years of experience as a Data Engineer.
• Strong SQL and Python skills; good understanding of Spark/PySpark.
• Experience building and maintaining production data pipelines.
• Practical experience working with cloud-based data warehouses and data lake architectures.
• Experience with AWS services for data processing (Glue, Athena, Kinesis, Lambda, S3, etc.).
• Familiarity with orchestration tools (Dagster, Airflow, Step Functions).
• Solid understanding of data modeling, and data quality best practices.
• Experience working with CI/CD pipelines or basic automation for data workflows.
• Exposure to Generative AI workflows or willingness to learn: embeddings, vector stores, enrichment pipelines, LLM-based data improvements, 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.