Data Engineer (Snowflake / Databricks / BigQuery)

Role overview

Responsibilities

• Build scalable ingestion pipelines for structured, semi-structured, and unstructured sources, including APIs, databases, and application logs.
• Develop ELT/ETL models using Python, SQL, and frameworks such as dbt, Apache Spark, or PySpark.
• Optimize cloud data platforms, schemas, clustering, partitioning, and storage in Snowflake, Databricks, or BigQuery.
• Configure automated orchestration, schedules, error handling, and dependencies using tools such as Airflow, Prefect, or Mage.
• Build automated data-quality and validation checks for latency, constraints, row balances, and anomalies.
• Tune query performance and cluster costs, including SQL joins, micro-partitioning, and execution bottlenecks.
• Configure data access and compliance controls, including row- and column-level security, masking, and RBAC.

Experience and qualifications

• 4–8 years of database engineering or backend development experience, including 3+ dedicated years building and maintaining enterprise-scale data infrastructure.
• Strong advanced SQL optimization and Python programming skills, with experience in data modeling and cloud storage.
• Understanding of distributed computing, big-data architectures, stream-processing constraints, and cloud-resource pricing.
• At least one certification is mandatory: Snowflake Core Data Engineer, Databricks Data Engineer Professional, or Google Cloud Professional Data Engineer.

Preferred qualifications

• Legacy data warehouse migrations to modern cloud data lakes are preferred.
• Familiarity with Kafka, Flink, AWS Kinesis, Google Pub/Sub, or other cloud-native event systems is preferred.

Workplace and employment

• The posting explicitly identifies the role as remote.
• The detailed job description identifies employment type as contract.