Data Engineer

Role Overview

Own the design, implementation, and scaling of core data infrastructure, including high-throughput batch and streaming pipelines and analytics-ready data models.

Responsibilities

• Design and build scalable data pipelines (Python, Scala, and/or SQL) to ingest data from diverse internal and external sources into the data warehouse.
• Manage and optimize modern cloud data warehouses and data lakes using Snowflake, Databricks, or BigQuery.
• Use dbt to implement data modeling best practices and transform raw data into clean, tested, documented datasets.
• Monitor pipeline health and reliability; proactively identify and resolve bottlenecks, failures, and data quality regressions.
• Collaborate with backend engineers to define logging and instrumentation standards for new product features.
• Write clean, testable, well-documented code and participate in code and architecture reviews.

Requirements

• 3–6 years of data engineering experience building and maintaining production-grade pipelines and data warehouses.
• Strong SQL and Python skills for data manipulation and orchestration.
• Experience with workflow orchestration tools such as Airflow, Prefect, or Dagster.
• Hands-on experience with cloud data warehousing platforms like Snowflake, BigQuery, or Redshift, plus dbt.
• Understanding of distributed processing frameworks such as Apache Spark or Ray.
• Bachelor’s degree in Computer Science/Statistics/Mathematics (or equivalent practical experience).

Nice to Haves

• Streaming architecture experience with Kafka or Flink.
• Infrastructure-as-code experience with Terraform.