Senior Data Engineer
SRM Technologies· enterprise· Information Technology And Services· Oct 6, 2026 · 21d ago
United States
Remote
Full-time
Senior
$120K–$176K est.
8+ years exp
Job description
This is a remote position.
Summary:
Role: Senior Data Engineer
Experience: 8+ Years
Mandatory/Core: Python, PySpark, Snowflake, dbt, Apache Iceberg, AWS, SQL
Preferred: AWS Glue, S3, EMR, Lambda, Airflow, Snowpipe/Snowpark, CI/CD, Terraform, Data Modeling
Role Type: Senior hands-on Data Engineer
Focus: Cloud Data Engineering, Lakehouse, Data Transformation, Performance Optimization and Production Engineering
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:
Area
Expected Capability
Python
Advanced, production-quality data engineering development
PySpark
Large-scale distributed processing, optimization and troubleshooting
Snowflake
Development, modeling, optimization and performance tuning
dbt
Models, tests, macros, documentation and deployment practices
Apache Iceberg
Lakehouse/table design, partitioning, schema evolution and optimization
AWS
Hands-on cloud data platform development
SQL
Advanced SQL, query optimization and analytical processing
Data Engineering
ETL/ELT, batch/incremental pipelines, data quality and orchestration
Data Architecture
Data Lake, Data Warehouse and Lakehouse concepts
Engineering Practices
Git, testing, code reviews, CI/CD and production support
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.
Application prep
Use the role’s language and application details while tailoring your materials.
Keywords to mirror
pythonawssqlci/cddockeragilescrum
- Platform
- zoho-recruit
- Effort
- medium