Data Engineer

About The Role The role owns the design, implementation, and scaling of core data infrastructure and high-throughput pipelines that power analytical workloads across the business. The team works closely with data analysts, backend engineers, and product stakeholders to ensure reliable data ingestion, transformation, and accessibility.

Key Responsibilities Design, build, and optimize scalable ETL and ELT data pipelines using Python, SQL, and Apache Spark

Manage cloud data warehousing infrastructure (Snowflake, BigQuery, or Redshift) and enforce dimensional modeling best practices

Implement automated data quality monitoring, anomaly detection, and schema migration frameworks

Collaborate with analytics and engineering teams to define data contracts, optimize query performance, and reduce cloud compute costs

Write clean, version-controlled code using Git, and participate in code reviews and infrastructure design discussions

What We Are Looking For 3-6 years of experience in data engineering, backend development, or analytics engineering with a focus on pipeline orchestration

Advanced SQL and Python proficiency; hands-on experience with workflow orchestrators like Apache Airflow or Prefect

Proven experience building and maintaining data warehouses in cloud environments such as AWS, GCP, or Azure

Solid understanding of distributed computing concepts, data modeling (Kimball methodology), and performance tuning

Bonus: Experience with real-time streaming tools (Kafka, Flink) and Infrastructure-as-Code (Terraform)

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