Data Engineer – Snowflake, Databricks, BigQuery

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. Design and engineer highly scalable data ingestion pipelines for structured, semi-structured, and unstructured data from APIs, databases, and application logs .

Posted 9/23/2026contractRemote • IndiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and engineering scalable data ingestion pipelines and optimizing cloud data platforms. Proficient in advanced SQL optimization, Python programming, and implementing data quality measures.

Highest-signal resume keywords

Snowflake Certified Core Data EngineerDatabricks Certified Data Engineer ProfessionalGoogle Cloud Certified Professional Data EngineerAdvanced SQL OptimizationData Pipeline Design

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Python ProgrammingSQL OptimizationData ModelingELT/ETL TransformationData IngestionData Quality AssuranceCloud Data ArchitectureDistributed Computing PrinciplesData Stream ProcessingData Warehouse Migration

Tools & Technologies

Apache SparkPySparkSnowflakeDatabricksBigQueryApache AirflowPrefectMageApache KafkaAWS Kinesis

Certifications & Qualifications

Snowflake Certified Core Data EngineerDatabricks Certified Data Engineer ProfessionalGoogle Cloud Certified Professional Data Engineer

Industry Keywords

Data Ingestion PipelinesEnterprise Data InfrastructureCloud StorageData Quality GatesRBAC Access ControlStar SchemaSnowflake SchemaData VaultMicro-PartitioningEvent Systems

Tech Stack

Tools & technologies

AirflowApacheAWSBigQueryCloudETLKafkaPySparkPythonSparkSQLVault

About the role

Key responsibilities & impact

Design and engineer highly scalable data ingestion pipelines for structured, semi-structured, and unstructured data from APIs, databases, and application logs
Develop complex ELT/ETL transformation models using Python, SQL, dbt, Apache Spark, and PySpark
Architect and optimize enterprise cloud data platforms in Snowflake, Databricks, or BigQuery
Design optimized schemas, clustering keys, partition strategies, and storage parameters
Implement automated data orchestration pipelines with workflow schedules, error-handling paths, and dependency graphs using Apache Airflow, Prefect, or Mage
Establish data quality and validation gates
Monitor data latency, validate structural constraints, check row balances, and enforce data anomaly alerts
Optimize query performance and cluster costs
Audit resource utilization, restructure inefficient SQL joins, manage micro-partitioning schemas, and tune execution bottlenecks
Govern data platform access and compliance layers
Configure row- and column-level security, data masking rules, and RBAC access control policies

Requirements

What you’ll need

4 to 8 years of core database engineering or backend development experience
3+ dedicated years actively building and maintaining enterprise-scale data infrastructure footprints
3+ years of dedicated data engineering experience designing pipelines and analytics data warehouses
Mandatory certification: Snowflake Certified Core Data Engineer, Databricks Certified Data Engineer Professional, or Google Cloud Certified Professional Data Engineer
Strong technical mastery of advanced SQL optimization
Python programming proficiency
Experience with relational/dimensional data modeling, including Star/Snowflake schemas and Data Vault
Experience with cloud storage setups
Deep understanding of distributed computing principles
Understanding of big data architectures
Understanding of data stream processing constraints
Understanding of cloud resource pricing structures
Prior experience managing large-scale legacy data warehouse migrations over to modern cloud data lakes is preferred
Familiarity with streaming architectures using Apache Kafka, Flink, AWS Kinesis, Google Pub/Sub, or similar cloud-native event systems is preferred

Benefits

Comp & perks

Remote work arrangement
Contract employment