AWS Glue & PySpark development
Build Spark DataFrame and DynamicFrame jobs for ingestion, cleansing, joins, aggregations, schema handling, partitioned output, data catalog integration and scheduled ETL.
AWS Glue Consultant · PySpark Programmer · AWS Batch & GPU Data Engineer
Build, repair or scale AWS ETL pipelines for large datasets.
Hire one senior hands-on engineer for AWS Glue and PySpark ETL, Amazon S3 event ingestion, Lambda orchestration, ECS Fargate containers, AWS Batch jobs, EC2 GPU workloads, Python, complex SQL, DevOps and production troubleshooting.
- Transform high-volume structured or unstructured data with AWS Glue, PySpark, Spark SQL and Python.
- Route S3 files through Lambda validation and orchestration into the right heavy-processing service.
- Run containerized ETL and data-processing jobs on ECS Fargate or AWS Batch.
- Use EC2 GPU or EC2-backed AWS Batch for clustering, machine learning and compute-intensive data workloads.
- Troubleshoot failed, slow, expensive or difficult-to-operate production pipelines.
Best fit: companies with real data volume, production ETL, batch-processing or pipeline reliability problems that need senior architecture and hands-on implementation together.
10+ yearsHands-on senior Python engineering
100M+Vector records administered
MillionsRecords processed in AWS data workflows
60+ accountsEnterprise AWS architecture and operations
End-to-endRequirements, architecture, code, CI/CD and support
Commercial AWS data engineering services
One owner across ingestion, transformation, compute and operations.
The service is structured for buyers searching for AWS Glue consulting, PySpark programmers, large-scale ETL development, S3 data pipelines, AWS Batch jobs and GPU processing on AWS.
Lambda triggered by Amazon S3
Validate new objects, route file types, enrich metadata, enforce idempotency and start heavier Glue, Fargate or Batch processing without forcing large transformations into Lambda.
ECS Fargate data jobs
Run containerized Python processing with custom libraries, predictable isolation and no EC2 cluster management for workloads that fit Fargate resource and runtime constraints.
AWS Batch job engineering
Design job definitions, queues, compute environments, array jobs, retries, dependency chains, container images, CloudWatch visibility and cost-aware EC2 or Fargate execution.
EC2 GPU data processing
Automate GPU-backed workloads for clustering, vector processing, ML preparation and compute-heavy jobs using EC2-backed AWS Batch or ECS with appropriate CUDA-enabled images.
Pipeline troubleshooting
Investigate failed Glue jobs, slow PySpark stages, partition skew, expensive shuffles, memory pressure, unreliable retries, S3 event duplication, container failures and missing observability.
Unsure whether the workload belongs in Glue, Lambda, Fargate, AWS Batch or EC2 GPU?
Share the data size, arrival pattern, transformation complexity, runtime and current bottleneck. I will assess the complete pipeline rather than recommend one service for every workload.
Workload-to-compute decisions
Use the right AWS service for the data shape and runtime.
Large-data systems fail when every task is treated as the same kind of compute. The architecture should separate event handling, distributed ETL, custom containers, high-scale batch and GPU acceleration.
Distributed transformations
Best for large joins, aggregations, cleansing, repartitioning, Spark SQL and ETL that benefits from distributed workers.
Inputs: S3, JDBC sources, catalogsOutputs: S3 data lakes, Parquet, JDBC targets
Fast event handling
Best for validation, routing, metadata extraction, small transformations and starting downstream jobs when objects arrive.
Avoid: using Lambda as the default engine for very large or long-running transformations
Custom serverless containers
Best for containerized processing that needs custom system packages or libraries but does not need direct server management or GPU resources.
Good fit: repeatable Python jobs, scheduled tasks, custom binaries
High-scale and heavy jobs
Best for large numbers of container jobs, high memory or CPU requirements, long runtimes, Spot capacity and dependency-driven batch workflows.
Good fit: array jobs, backfills, large independent work units
Accelerated computation
Best for CUDA-enabled clustering, model workloads, vector processing and algorithms that can use NVIDIA GPU acceleration.
Important: GPU jobs use EC2-backed compute; Fargate job definitions do not support GPU resources.
Orchestration and recovery
Coordinate job starts, dependencies, retries, timeouts, callbacks and operational states across Lambda, Glue, Fargate, Batch and EC2.
Goal: observable workflows instead of hidden shell scripts and manual reruns
SourcesFiles · APIs · DatabasesStructured and unstructured inputs
Amazon S3Landing & data lakeRaw, staged and curated zones
RoutingLambda · EventsValidate, classify and orchestrate
ComputeGlue · Fargate · BatchPySpark, Python and containers
DestinationsS3 · Aurora · RedshiftAnalytics, applications and AI
Production deliverables
Finished pipelines—not only notebooks or architecture slides.
Delivery can include requirements analysis, architecture diagrams, Python or PySpark code, infrastructure as code, container images, CI/CD, tests, monitoring, runbooks and production troubleshooting.
- Data-source and destination mapping with volume, frequency and SLA assumptions
- PySpark ETL code, Spark SQL, validation, schema handling and partition strategy
- S3 event ingestion, Lambda orchestration and duplicate-event protection
- Docker images, Fargate tasks, Batch job definitions and compute environments
- CloudWatch logs, metrics, alerts, retry behavior and failure-recovery procedures
- GitHub Actions, Terraform, CloudFormation or AWS CDK deployment automation
Distributed ETLAWS Glue · PySpark · Spark SQL
Event IngestionAmazon S3 · Lambda · EventBridge
ContainersDocker · ECS Fargate · AWS Batch
Accelerated ComputeEC2 GPU · CUDA · Batch GPU
Data StoresS3 · Aurora PostgreSQL/MySQL · Redshift
OperationsCI/CD · CloudWatch · SRE · Cost control
Senior engineering proof
Data engineering backed by Python, AWS architecture and production operations.
The value is not a list of isolated services. It is the ability to translate requirements into an AWS design, implement the code, automate delivery and remain accountable for reliability and cost.
Senior Python Programmer
Hands-on Python engineering for ETL, algorithms, API integrations, asyncio, JSON processing, validation, Boto3 automation, complex SQL and maintainable production modules.
AWS Architect & Data Engineer
Architecture across S3, Lambda, Glue, ECS Fargate, AWS Batch, EC2, Aurora, Redshift, Step Functions, IAM, VPC networking and multi-account environments.
DevOps & SRE Ownership
GitHub Actions, CI/CD, Docker, Kubernetes, infrastructure as code, CloudWatch, production support, job recovery, security controls and cost-aware operations.
Experience claims kept specific.
The page uses the supplied portfolio facts—senior Python, AWS architecture, Lambda and Fargate expertise, ETL and complex SQL, 60+ AWS accounts, millions of records in AWS workflows and administration of a 100M-record vector database. It does not claim an active AWS certification.
Relevant data-intensive work
Large data, orchestration, GPU compute and production AWS.
Representative work selected from the supplied website and resume wording, reframed around the new AWS data-processing offer.
Persona extraction from millions of records
Open-source census and sales data was ingested through a monitored AWS workflow, grouped with GPU clustering algorithms and prepared for downstream RAG analysis.
- Large-volume data ingestion and transformation
- GPU-backed clustering workflow
- Pipeline monitoring and operational ownership
- Data preparation for analytics and retrieval
LLM orchestration and vector data
Python, Step Functions, APIs, background jobs and vector databases supporting RAG and LLM workflows across a very large record set.
- Approximately 100 million vector records administered
- Python orchestration and data-processing steps
- GPU instances and local model execution
- CloudWatch monitoring and automated workflows
Multi-account automation and governance
Lambda-driven billing, tagging, IAM key rotation, security enforcement and operational automation across a large AWS organization.
- Event-driven Python automation
- Security and governance controls
- CI/CD and infrastructure delivery
- Production troubleshooting across accounts
High-frequency API and ETL workflows
Request validation, JSON cleanup, queue-based decoupling and downstream processing into PostgreSQL and Redshift-oriented data flows.
- Python validation and transformation
- Asynchronous and queue-driven processing
- Complex SQL and relational integrations
- End-to-end pipeline architecture
Ways to engage
Start with the smallest useful scope.
Engagements can be fixed-milestone or weekly, and can begin with a small demo, review or test job before a larger pipeline implementation.
Pipeline architecture review
Review data sources, volume, transformations, existing jobs, costs, bottlenecks, security and operational gaps; deliver an actionable service and implementation plan.
Focused build or repair sprint
Implement or fix a defined Glue, PySpark, S3/Lambda, Fargate, AWS Batch, GPU, Python, SQL or DevOps deliverable with testing and deployment support.
Ongoing senior ownership
Own pipeline architecture, coding, infrastructure, CI/CD, monitoring, cost optimization, incident response and technical leadership over a longer engagement.
Technology and search-intent coverage
AWS-native tools for production data processing.
These terms are included naturally because they describe the actual service scope; they are not repeated as artificial keyword stuffing.
AWS GluePySparkApache SparkSpark SQLDynamicFramesDataFramesAWS Glue Data CatalogAWS Glue CrawlersAmazon S3AWS LambdaS3 Event NotificationsAmazon EventBridgeAWS Step FunctionsAmazon ECSAWS FargateAWS BatchBatch Array JobsAmazon EC2EC2 GPUCUDADockerPythonBoto3Complex SQLETLELTData PipelinesBatch ProcessingDistributed ProcessingParquetJSONCSVAurora PostgreSQLAurora MySQLAmazon RedshiftCloudWatchGitHub ActionsCI/CDTerraformAWS CDKCloudFormationIAMVPCCost OptimizationProduction Support
Before you contact me
Common AWS data-pipeline questions.
These answers qualify the workload and reduce unnecessary discovery calls.
Can you work with existing Glue or PySpark jobs?
Yes. I can review and improve existing code, job settings, partitioning, joins, memory use, retries, observability, deployment and cost.
Can you process data arriving in S3?
Yes. S3 events can trigger Lambda validation and routing, then start Glue, Fargate or Batch workloads for heavier transformations.
Do you implement, or only advise?
I provide architecture and hands-on delivery: Python/PySpark code, infrastructure, containers, CI/CD, tests, monitoring and production troubleshooting.
Can you handle long-running or high-memory jobs?
Yes. Workloads can be designed for AWS Glue workers, Fargate where appropriate, or EC2-backed AWS Batch for heavier CPU, memory or runtime requirements.
Can you run GPU jobs on Fargate?
Fargate job definitions do not support GPU resources. GPU processing should use EC2-backed AWS Batch or ECS with suitable GPU instances and images.
What should I include in the first message?
Share data sources and destinations, approximate volume, arrival frequency, transformation logic, current AWS services or errors, SLA and preferred timeline.
Senior AWS data engineering ownership
Tell me what the pipeline must process—and what is failing today.
Available for AWS Glue and PySpark development, S3/Lambda ingestion, Fargate and AWS Batch jobs, EC2 GPU workloads, Python ETL, complex SQL, architecture reviews, performance tuning, CI/CD and production support.
For a useful first response, include: source and destination systems, data volume and frequency, file formats, current architecture, job duration or errors, security constraints and required timeline.