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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 .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.