Senior Data Engineer

This is a remote position.

Summary:

Detailed information:

Senior Data Engineer:

Experience: 8+ years of overall Data Engineering experience, with strong hands-on experience building enterprise-scale cloud data platforms and pipelines.

Primary Skills:

• Python
  
• PySpark / Apache Spark
  
• Snowflake
  
• dbt (Data Build Tool)
  
• Apache Iceberg
  
• AWS Data Services
  
• Advanced SQL
  
• Data Engineering / ETL / ELT
  
• Data Lake / Lakehouse architecture
  

Secondary / Preferred Skills:

• AWS services such as:
  
• S3
  
• AWS Glue
  
• EMR
  
• Lambda
  
• Step Functions
  
• CloudWatch
  
• IAM
  
• Apache Airflow or other workflow orchestration tools
  
• Snowflake performance optimization and cost optimization
  
• Snowpipe / Snowpark
  
• Spark performance tuning
  
• Data modeling and dimensional modeling
  
• Parquet and other columnar data formats
  
• Data quality frameworks and automated validation
  
• CI/CD for data pipelines
  
• Git / GitHub / GitLab
  
• Infrastructure as Code such as Terraform or AWS CDK
  
• Docker / containerization
  
• Data governance, lineage, security, and access control
  
• Agile/Scrum delivery experience
  

Job Description:

We are looking for a Senior Data Engineer with strong hands-on expertise in Python, PySpark, Snowflake, dbt, Apache Iceberg, and AWS to design, develop, and maintain scalable enterprise data solutions.

The candidate should have strong experience working with high-volume data processing, cloud-based data platforms, modern lakehouse architectures, ETL/ELT pipelines, data modeling, performance optimization, and production-grade engineering practices.

The ideal candidate should be capable of independently owning complex data-engineering components, contributing to technical design and architecture decisions, troubleshooting production issues, and providing technical guidance to other engineers.

Key Responsibilities:

1. Data Pipeline Engineering

• Design, develop, test, and maintain scalable ETL/ELT data pipelines.
  
• Develop production-quality data-processing solutions using Python and PySpark.
  
• Build reusable frameworks and components for ingestion, transformation, validation, and publishing of data.
  
• Process large structured, semi-structured, and distributed datasets.
  
• Implement incremental and batch-processing patterns where appropriate.
  

2. Snowflake Development

• Design and develop scalable data solutions using Snowflake.
  
• Develop complex SQL transformations, data models, views, and reusable data structures.
  
• Optimize Snowflake workloads for performance, scalability, and cost.
  
• Implement appropriate data-loading and transformation patterns between AWS data platforms and Snowflake.
  
• Troubleshoot performance and data-quality issues across Snowflake workloads.
  

3. dbt Development

• Build and maintain transformation pipelines using dbt.
  
• Develop modular, reusable, maintainable dbt models.
  
• Implement dbt tests and documentation.
  
• Follow appropriate development practices for source, staging, intermediate, and business-layer transformations.
  
• Support automated deployment and CI/CD practices for dbt projects.
  

4. Apache Iceberg / Lakehouse

• Design and implement data-lake and lakehouse solutions using Apache Iceberg.
  
• Build scalable table structures for large analytical datasets.
  
• Work with partitioning, schema evolution, incremental processing, and table-maintenance strategies.
  
• Integrate Iceberg-based datasets with Spark and AWS-based data-processing services.
  
• Ensure efficient storage and query patterns for high-volume datasets.
  

5. AWS Data Engineering

• Design and implement cloud-native data solutions on AWS.
  
• Build data-processing workloads leveraging services such as S3, Glue, EMR and Lambda where appropriate.
  
• Implement secure access patterns using AWS IAM.
  
• Monitor data workloads and troubleshoot operational issues.
  
• Participate in designing scalable, reliable, secure, and cost-efficient cloud data architectures.
  

6. Performance & Scalability

• Diagnose and optimize Spark/PySpark jobs, SQL queries, Snowflake workloads, and data pipelines.
  
• Identify bottlenecks involving compute, storage, partitioning, data skew, transformations, and queries.
  
• Design solutions capable of supporting increasing data volumes without unnecessary infrastructure cost.
  

7. Data Quality & Governance

• Implement automated data-quality checks across ingestion and transformation layers.
  
• Establish proper logging, monitoring, exception handling, and reconciliation mechanisms.
  
• Follow organizational standards for data security, governance, lineage, and access controls.
  
• Ensure production pipelines are reliable, auditable, and maintainable.
  

8. Engineering Best Practices

• Write clean, modular, reusable, testable, and maintainable code.
  
• Perform code reviews and enforce engineering standards.
  
• Implement unit, integration, and data-validation testing.
  
• Use Git-based version control and CI/CD practices.
  
• Create and maintain appropriate technical documentation.
  

9. Senior-Level Responsibilities

• Independently drive technically complex data-engineering requirements from design through production deployment.
  
• Participate in solution design and architecture discussions.
  
• Evaluate alternative implementation approaches and recommend appropriate solutions.
  
• Troubleshoot complex production and performance issues.
  
• Mentor junior and mid-level data engineers.
  
• Collaborate with Architects, Product Owners, Business Analysts, Data Scientists, QA, DevOps, and application teams.
  
• Translate business/data requirements into scalable technical solutions.
  
• Identify technical risks and proactively recommend improvements.
  

Core Skills Expected

A strong candidate should demonstrate deep hands-on capability, not merely theoretical exposure, in the following areas:

Preferred Qualifications

• Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related discipline.
  
• Strong experience delivering enterprise-scale cloud data platforms.
  
• Experience migrating legacy data workloads to modern AWS/Snowflake architectures.
  
• Experience working with very large datasets and distributed processing.
  
• Knowledge of data security and governance practices.
  
• Experience working in Agile delivery environments.
  
• AWS and/or Snowflake certification is an added advantage.