Browse categories
Explore
Fiverr Pro
English
$
USD
I will build etl data pipelines using AWS, spark, airflow
About this Gig
Build scalable ETL data engineering pipelines for cloud and on-prem systems.
Struggling with messy data or slow workflows? I design and implement end-to-end ETL and ELT pipelines that automate data ingestion, transformation, validation, and loading across modern cloud platforms.
Using tools like Spark, Python, SQL, Airflow, Snowflake, Databricks, AWS, and GCP, I build production-ready data pipelines that turn raw data into reliable analytics infrastructure.
What I offer:
- ETL and ELT pipelines (batch or streaming)
- API, database, and cloud storage integrations
- Cloud-native deployment: AWS Glue, Lambda, Redshift, Azure Data Factory, Synapse, Databricks, GCP Dataflow, BigQuery
- Big Data Tech Stack: Expert implementation of Kafka, Hadoop, and Hive.
- Orchestration & Automation: Airflow or Dagster.
Why choose me?
- Clean, maintainable code with clear documentation
- Strong communication and transparent project scoping
- Experience working with modern cloud and big data stacks
I focus on building data systems that are reliable, cost-efficient, and easy to extend - not just moving data.
Note: Please message me before ordering so we can align on requirements and scope your project correctly.
FAQ
Which cloud providers do you work with?
I am proficient in all major cloud ecosystems, including AWS (Glue, Redshift, EMR, S3), Azure (Data Factory, Synapse, Databricks), and Google Cloud Platform (BigQuery, Dataflow). I can also build on-premise solutions using open-source tools like Docker and Kubernetes.
How do you ensure the data is accurate and clean?
I implement a multi-layered Data Quality approach. This includes schema validation at the ingestion point, automated unit tests for transformation logic, and monitoring alerts that notify us immediately if data drift or anomalies occur.
Will the pipeline be expensive to run in the cloud?
Performance tuning is a core part of my service. I optimize Spark jobs (partitioning, caching, and shuffling) and choose the right compute instances to ensure your pipeline is as cost-effective as possible. I aim for maximum throughput with minimum resource consumption.
Can you handle real-time data streaming?
Yes. For sub-second latency requirements, I use Apache Kafka or AWS Kinesis combined with Spark Streaming or Flink. I can architect systems that process data the moment it’s generated, perfect for live dashboards or IoT applications.
What do you need to get started?
I’ll need a clear understanding of your data sources (APIs, Databases, CSVs), the destination (Warehouse, Data Lake), and the business logic for transformations. If we are working in the cloud, I will also need temporary IAM access or a collaborative environment to deploy the infrastructure.
Do you provide documentation for the architecture?
Absolutely. Every project includes technical documentation covering the system architecture, data lineage, and instructions on how to maintain or scale the pipeline. For Premium orders, I provide a detailed Data Dictionary.
