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I will automate API ingestion into bigquery with python
Cloud Data Engineer, BigQuery, Snowflake, dbt, Python, ETL
Senior Data Engineer specializing in scalable cloud data platforms and production-ready ETL/ELT pipelines.
I help businesses build reliable data solutions using AWS, BigQuery, Snowflake, dbt, Python,...
Über diesen Service
Build a scalable and production-ready ETL pipeline from APIs, CSV, JSON, databases, or cloud storage directly into Google BigQuery.
I specialize in Python-based automated data pipelines for analytics, reporting, Power BI, Looker Studio, Tableau, and business intelligence platforms.
Services include:
API BigQuery ingestion
Incremental data loading
Historical backfill
JSON / CSV normalization
Automated scheduled pipelines
AWS Lambda / serverless architecture
Retry & error handling
Logging & monitoring
Data deduplication
Partitioned BigQuery tables
Raw Staging Curated architecture
dbt-ready warehouse structures
Technologies:
- Python
- BigQuery
- AWS Lambda
- S3 / GCS
- Airflow / Prefect
- dbt
- REST APIs
Typical use cases:
- Ecommerce analytics
- Financial reporting
- Marketing dashboards
- CRM integrations
- Automated reporting systems
I focus on scalable, maintainable, and production-ready architectures rather than simple scripts.
Please contact me before ordering for custom or large-scale projects.
Revisions do not include major scope changes or additional integrations.
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FAQ
Do you support large datasets?
Yes. I design scalable pipelines for millions of records and production workloads.
Can you deploy on AWS?
Yes. I can deploy serverless architectures using Lambda, S3, Step Functions, and CloudWatch.
Can you optimize BigQuery costs?
Yes. I use partitioning, clustering, incremental processing, and optimized query patterns.
Which architecture do you prefer for your data pipeline?
I can build the pipeline using either AWS-native or GCP-native architecture depending on your existing infrastructure, budget, and reporting requirements. 1. API → Cloud Run / Cloud Function → GCS Raw → BigQuery 2. API → Lambda → S3 Raw → BigQuery Data Transfer Service → BigQuery
Can you build incremental ETL pipelines?
Yes. I strongly prefer incremental processing over full reloads for scalability, lower BigQuery costs, and improved reliability.
Do you support dbt transformations?
Yes. I can create dbt models for staging, cleaning, joins, business logic, and curated analytics tables.
Can you work with existing data warehouses or pipelines?
Yes. I can improve, optimize, debug, or extend existing BigQuery, AWS, or ETL environments.
Can you integrate Power BI or other BI tools?
Yes. I can prepare analytics-ready datasets optimized for Power BI, Looker Studio, Tableau, and SQL analytics.
Do you provide monitoring and error handling?
Yes. Production pipelines include logging, retries, alerts, and monitoring to improve reliability and operational stability.
Can you handle historical backfills and large API datasets?
Yes. I can build pipelines for historical synchronization, paginated APIs, and large-scale datasets with optimized loading strategies.
