Senior Data Engineer
# Senior Data Engineer **SRM Technologies** · Remote · `Remote` 🕒 **Статус:** *Опубликовано: сегодня* · *Источник: Indeed* --- ### About the Role Job Information - Date Opened 10/06/2026 - Job Type Full time - Remote Job - Industry Technology 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.
Наблюдалась 2026-10-07, впервые 2026-10-06, источник — Indeed.