Data Engineer – Mid & Senior Level
# Data Engineer – Mid & Senior Level **Synthlane Technologies Private Limited** · United States · `Remote` · `Full Time` 💼 **Уровень роли:** `Senior` 🕒 **Статус:** *Опубликовано: сегодня* · *Источник: Himalayas (JSON API)* --- ### Top Skills & Match 🎯 **Ключевой стек роли:** `[Data-Engineer]` `[Mid-Level-Data-Engineer]` `[Senior-Data-Engineering]` `[Senior-Lead-Data-Engineering]` --- ### About the Role This is a remote position. Role Overview We are looking for Data Engineers at Senior and Mid-Level to join our team in building a privacy-preserving data platform where data engineering meets production-grade software engineering. You will work on designing, developing, and maintaining reliable data pipelines that transform operational data into high-quality, secure, and AI-ready datasets. Key Responsibilities - Build and maintain production-grade data pipelines on AWS. - Extract, transform, validate, and curate large-scale Parquet datasets. - Implement data de-identification, masking, and privacy-preserving transformations. - Design and maintain data pipeline orchestration, scheduling, retries, and backfill mechanisms. - Implement comprehensive data quality checks, monitoring, and alerting. - Work with workflow orchestration tools such as Airflow, Dagster, or AWS Step Functions. - Contribute to CI/CD pipelines and Infrastructure as Code (IaC) practices. - Manage schema evolution and schema drift across data sources and pipelines. - Provide production support, troubleshooting, and root cause analysis for data pipeline issues. - Maintain data catalogs, metadata, and data lineage. - Follow software engineering best practices including Git, code reviews, automated testing, and maintainable code. - Build reliable and idempotent data pipelines capable of handling retries and large-scale backfills. Required Skills & Experience - Strong proficiency in Python and SQL. - Hands-on experience with AWS data services and production data pipelines. - Experience with Apache Spark or equivalent distributed data processing technologies. - Practical experience with Airflow, Dagster, AWS Step Functions, or similar orchestration tools. - Strong understanding of data pipeline architecture, ETL/ELT, and data transformation. - Experience working with Parquet and large-scale datasets. - Understanding of data quality, schema management, monitoring, and alerting. - Strong software engineering practices including: - Git and version control - Code reviews - Automated testing - Idempotency - Error handling - Retries and backfills - Experience supporting and troubleshooting production data pipelines. - Ability to work effectively with cross-functional engineering and data teams. Requirements Required Skills Python | SQL | AWS | Data Engineering | Data Pipelines | ETL/ELT | Apache Spark | Parquet | Airflow | Dagster | AWS Step Functions | Data Orchestration | Data Quality | Schema Management | Data Transformation | Production Support | Git | CI/CD | Automated Testing | Data De-identification | Data Lineage | Data Catalog Good to Have Debezium | AWS DMS | Apache Iceberg | Delta Lake | Apache Hudi | Data Masking | Data Tokenization | Terraform | CloudFormation | ML/AI Training Data | Privacy-Preserving Data Originally posted on Himalayas
- Data-Engineer
- Mid-Level-Data-Engineer
- Senior-Data-Engineering
- Senior-Lead-Data-Engineering
Наблюдалась 2026-10-12, впервые 2026-10-12, источник — Himalayas (JSON API).