You will get optimized Google BigQuery queries with lower processing costs
Project details
I will analyze and optimize your Google BigQuery queries to reduce processing costs and improve execution time.
You will receive optimized SQL together with a clear explanation of the changes and practical recommendations for partitioning, clustering, incremental processing, table design, and workload management.
I have extensive experience working with enterprise BigQuery environments, large analytical datasets, Apache Airflow pipelines, and cost-sensitive workloads. I focus not only on making queries faster, but also on keeping the solution reliable, readable, and maintainable.
Depending on the selected package, I can review individual queries or a broader workload, including SELECT statements, MERGE operations, joins, full-table scans, partition pruning, slot consumption, and data model design.
Deliverables may include:
• Optimized BigQuery SQL
• Explanation of identified bottlenecks
• Estimated reduction in processed data
• Partitioning and clustering recommendations
• Incremental processing recommendations
• A prioritized technical summary
No production access is required in most cases. You can provide anonymized SQL, table schemas, and execution details.
You will receive optimized SQL together with a clear explanation of the changes and practical recommendations for partitioning, clustering, incremental processing, table design, and workload management.
I have extensive experience working with enterprise BigQuery environments, large analytical datasets, Apache Airflow pipelines, and cost-sensitive workloads. I focus not only on making queries faster, but also on keeping the solution reliable, readable, and maintainable.
Depending on the selected package, I can review individual queries or a broader workload, including SELECT statements, MERGE operations, joins, full-table scans, partition pruning, slot consumption, and data model design.
Deliverables may include:
• Optimized BigQuery SQL
• Explanation of identified bottlenecks
• Estimated reduction in processed data
• Partitioning and clustering recommendations
• Incremental processing recommendations
• A prioritized technical summary
No production access is required in most cases. You can provide anonymized SQL, table schemas, and execution details.
Database Type
MS SQLWhat's included
| Service Tiers |
Starter
$150
|
Standard
$350
|
Advanced
$700
|
|---|---|---|---|
| Delivery Time | 2 days | 4 days | 7 days |
Number of Revisions | 0 | 1 | 2 |
Source Code |
About Serikbol
Data Engineer | Airflow, BigQuery, GCP | ETL Pipelines Expert
Almaty, Kazakhstan - 7:59 pm local time
I am a Senior Data Engineer with 7+ years of hands-on experience in enterprise data platforms, ETL/ELT development, cloud migration, data quality, and business intelligence infrastructure.
My main areas of expertise:
• Building and supporting ETL/ELT pipelines with Apache Airflow, Python, SQL, Dataform, and dbt
• Developing data warehouses, ODS layers, and analytical data marts
• Migrating data and SQL workloads from MS SQL, Hadoop, and Impala to Google BigQuery
• Optimizing BigQuery queries using partitioning, clustering, incremental processing, TTL, and slot management
• Implementing CDC pipelines, idempotent MERGE operations, deduplication, and late-arriving data processing
• Designing automated data quality checks and source-to-target reconciliations
• Developing and supporting Apache Superset environments and BI integrations
• Processing large datasets with Apache Spark, PySpark, Hadoop, HDFS, and Impala
I have worked with enterprise analytical platforms containing approximately 70 TB of data and hundreds of automated Airflow workflows.
Typical tasks I can help with:
• Fixing or improving an existing Airflow DAG
• Building a new data pipeline
• Optimizing expensive BigQuery queries
• Creating incremental or CDC-based data models
• Migrating tables and reports to BigQuery
• Investigating data quality issues
• Setting up or troubleshooting Apache Superset
• Reviewing a data platform architecture and identifying performance bottlenecks
My core technology stack includes:
Google BigQuery, Google Cloud Storage, Apache Airflow, Python, SQL, Dataform, dbt, Apache Spark, PySpark, Hadoop, HDFS, Apache Impala, MS SQL Server, PostgreSQL, Apache Superset, Docker, Git, and CI/CD.
I focus on reliable, maintainable solutions with clear logging, monitoring, documentation, and predictable operating costs.
Steps for completing your project
After purchasing the project, send requirements so Serikbol can start the project.
Delivery time starts when Serikbol receives requirements from you.
Serikbol works on your project following the steps below.
Revisions may occur after the delivery date.
I will review the SQL queries, table schemas
, execution details, and current processing costs.
I will identify full-table scans, inefficient joins
, missing partition filters, expensive MERGE operations, and other bottlenecks.