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Lead Data Software Engineer
- Pay
- Pay not listed
- Work setting
Remote — work setting source
Work setup in the employer’s posting: TELECOMMUTE
From the employer’s posting- Employment
- Not stated
What you’ll work on
Full postingBuild and orchestrate ETL pipelines with Airflow
Optimize BigQuery pipelines using partitioning, clustering and query tuning
Design high-load systems for throughput, backpressure and scalability
Build secure Python REST services with FastAPI and async patterns
Drive designs from RFCs to operational runbooks
From the employer’s posting
Develop end-to-end ETL pipelines with job orchestration frameworks like Airflow
Optimize data pipelines and workflows by leveraging BigQuery's advanced features like partitioning, clustering, and query tuning
Design high-load systems with a focus on throughput, backpressure, and scalability
Build secure REST services in Python 3.x with technologies like FastAPI and asynchronous patterns
Drive the design process from RFCs to full operational runbooks with clear documentation and ownership
Tools in this posting
- Python
- SQL
- AWS
- Azure
- BigQuery
- dbt
- Excel
- GCP
- Terraform
- Airflow
- FastAPI
Source — Tool mentions in context
We are seeking a Lead Data Software Engineer to lead the evolution and maturity of data solutions in a dynamic GCP environment. The role involves implementing orchestration with Airflow, optimizing data ingestion pipelines, and driving the delivery of scalable, high-performance systems. Responsibilities Improve architecture to enhance the scalability and maturity of data solutions Implement orchestration for workflows using Airflow for efficient processes Build secure REST services in Python 3.x with technologies like FastAPI and asynchronous patterns Optimize data pipelines and workflows by leveraging BigQuery's advanced features like partitioning, clustering, and query tuning Design high-load systems with a focus on throughput, backpressure, and scalability Utilize GCP services such as Cloud Functions, Pub/Sub, and Cloud Storage to build robust data solutions Develop end-to-end ETL pipelines with job orchestration frameworks like Airflow Drive the design process from RFCs to full operational runbooks with clear documentation and ownership Requirements 7+ years professional software experience; 4+ years of expertise in Python 3.x within production environments Knowledge of FastAPI and Pydantic, with proficiency in async patterns and secure REST APIs Competency in SQL, data modeling, and hands-on experience with BigQuery (partitioning, clustering, query optimizations) Production expertise in GCP services including Cloud Functions, Pub/Sub, Cloud Storage, IAM/Secret Manager, Cloud Build, or equivalent AWS/Azure stacks Background in designing high-load systems with solutions for backpressure, idempotency, and scalability Experience working on ETL pipelines using tools like Airflow for job orchestration Showcase of excellent communication, ownership, and problem-solving skills Nice to have Familiarity with parsing file formats at scale like CSV, Excel, or XML along with schema evolution/versioning techniques Understanding of data quality frameworks such as assertions or expectations and tools like dbt or Dataform Background in event-driven architectures or streaming technologies; experience with Beam/Dataflow is a plus Expertise in concurrency and performance profiling in Python using asyncio or multiprocessing Knowledge of IaC practices using Terraform or SRE methodologies Awareness of security concepts such as authentication/authorization, service accounts, and principles of least privilege
Posting record
Report an errorActively hiringFirst seen by us 2 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 1, 20262 days ago
- Recorded sightings
- 4
- Last seen by us
- Nov 4, 2026
- Employer says posted
- Sep 29, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
EPAM Systems is hiring
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Yes. We’ve followed 15 other Lead Data Software Engineer jobs there since November 2026. All 15 are still up.
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Job description
We are seeking a Lead Data Software Engineer to lead the evolution and maturity of data solutions in a dynamic GCP environment. The role involves implementing orchestration with Airflow, optimizing data ingestion pipelines, and driving the delivery of scalable, high-performance systems. Responsibilities Improve architecture to enhance the scalability and maturity of data solutions Implement orchestration for workflows using Airflow for efficient processes Build secure REST services in Python 3.x with technologies like FastAPI and asynchronous patterns Optimize data pipelines and workflows by leveraging BigQuery's advanced features like partitioning, clustering, and query tuning Design high-load systems with a focus on throughput, backpressure, and scalability Utilize GCP services such as Cloud Functions, Pub/Sub, and Cloud Storage to build robust data solutions Develop end-to-end ETL pipelines with job orchestration frameworks like Airflow Drive the design process from RFCs to full operational runbooks with clear documentation and ownership Requirements 7+ years professional software experience; 4+ years of expertise in Python 3.x within production environments Knowledge of FastAPI and Pydantic, with proficiency in async patterns and secure REST APIs Competency in SQL, data modeling, and hands-on experience with BigQuery (partitioning, clustering, query optimizations) Production expertise in GCP services including Cloud Functions, Pub/Sub, Cloud Storage, IAM/Secret Manager, Cloud Build, or equivalent AWS/Azure stacks Background in designing high-load systems with solutions for backpressure, idempotency, and scalability Experience working on ETL pipelines using tools like Airflow for job orchestration Showcase of excellent communication, ownership, and problem-solving skills Nice to have Familiarity with parsing file formats at scale like CSV, Excel, or XML along with schema evolution/versioning techniques Understanding of data quality frameworks such as assertions or expectations and tools like dbt or Dataform Background in event-driven architectures or streaming technologies; experience with Beam/Dataflow is a plus Expertise in concurrency and performance profiling in Python using asyncio or multiprocessing Knowledge of IaC practices using Terraform or SRE methodologies Awareness of security concepts such as authentication/authorization, service accounts, and principles of least privilege
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