Data Engineer (Snowflake / Databricks / BigQuery)

Zoho; United States; Remote; USD 120,000 - 170,000; Full time

Job summary

Fyerx is seeking an experienced Data Engineer to design, build, and optimize high-throughput data architectures and transformation pipelines. You will orchestrate scalable ELT/ETL processes and manage massive relational database models to deliver clean, structured data for BI and data science.

The role focuses on developing data ingestion pipelines from diverse sources, implementing automated orchestration with Airflow, and optimizing cloud platforms such as Snowflake, Databricks, and BigQuery

Qualifications

• 4 to 8 years of core data engineering or backend experience.
• 3+ years building large-scale data pipelines and data warehouses.
• Strong SQL and Python skills with cloud data platforms.
• Experience with ELT/ETL and data quality gates.

Responsibilities

• Design scalable data ingestion pipelines from APIs, databases, and logs.
• Develop ELT/ETL models using Python, SQL, dbt, Spark.
• Architect cloud data platforms with Snowflake, Databricks, or BigQuery.
• Implement automated orchestration with Airflow, Prefect, or Mage.
• Enforce data quality, validation, and monitoring across pipelines.
• Optimize query performance and control cloud costs.
• Govern data access and compliance in your data platform.

Skills

SQL optimization

Python programming

Data modeling

Distributed computing

ELT/ETL design

Tools

dbt

Apache Spark

PySpark

Snowflake

Databricks

BigQuery

Apache Airflow

Prefect

Mage

Job description

• Employment Type: Contract
• Work Mode: Remote
• Location: Offshore
• Total Experience Required: 4 to 8 years
• Relevant Experience Required: 3+ years of dedicated data engineering experience designing pipelines and analytics data warehouses

Job Summary

We are seeking an experienced Data Engineer to design, build, and optimize high-throughput data architectures and transformation pipelines. The ideal candidate will orchestrate scalable ELT/ETL processes, manage massive relational database models, optimize query workloads within enterprise cloud data lakes, and deliver clean, structured data layers to power downstream business intelligence and data science models.

Key Responsibilities

• Design and engineer highly scalable data ingestion pipelines to aggregate structured, semi-structured, and unstructured data streams from diverse enterprise sources (APIs, databases, application logs).
• Develop complex ELT/ETL data transformation models using Python, SQL, and processing frameworks (e.g., dbt, Apache Spark , PySpark ).
• Architect and optimize enterprise cloud data platforms , designing optimized schemas, clustering keys, partition strategies, and storage parameters in Snowflake , Databricks , or BigQuery .
• Implement automated data orchestration pipelines , configuring workflow schedules, error-handling paths, and dependency graphs using tools like Apache Airflow , Prefect , or Mage .
• Establish strict data quality and validation gates, writing automated scripts to monitor data latency, validate structural constraints, check row balances, and enforce data anomaly alerts.
• Optimize query performance and cluster costs, auditing resource utilization footprints, restructuring inefficient SQL joins, managing micro-partitioning schemas, and tuning execution runtime bottlenecks.
• Govern data platform access and compliance layers, configuring fine-grained row/column-level security models, data masking rules, and access control policies (RBAC) to ensure compliance with privacy laws.

Requirements

• 4 to 8 years of core database engineering or backend development experience, with 3+ dedicated years actively building and maintaining enterprise-scale data infrastructure footprints.
• Strong technical mastery of advanced SQL optimization, Python programming, relational/dimensional data modeling (Star/Snowflake schemas, Data Vault), and cloud storage setups.
• Deep structural understanding of distributed computing principles, big data architectures, data stream processing constraints, and cloud resource pricing structures.
• Prior experience managing large-scale legacy data warehouse migrations over to modern cloud data lakes.