Data Engineering in the United States
ETL pipelines, data warehouses, and event streams — built to process millions of records reliably with monitoring, retries, and clean lineage.
Who this is for
Engineering and data leaders who need data movement that doesn't break overnight.
What problem this solves
Most 'data pipelines' are cron jobs and prayer. They break silently, lose data on retries, and become impossible to debug after the original engineer leaves.
Why this matters specifically in the United States
US teams have the highest expectations for AI deliverables in the world: production-grade engineering, observable systems, security review, and contracts written in plain English. Most successful engagements are paid in USD on a fixed-scope basis or via Upwork's enterprise contract or directly with a US LLC entity.
What you get
- Apache Airflow DAGs (or equivalent) with documented lineage
- Data warehouse schema with versioned migrations
- Monitoring + alerting on data quality and SLA breaches
- Cost-controlled compute with right-sized instances and clear $/run accounting
How the engagement runs
- Source audit. Map every data source, refresh cadence, schema-drift risk, and SLA expectation.
- Architecture. Choose batch vs. streaming, warehouse vs. lakehouse, with cost projections per scenario.
- Build. DAGs, schemas, transformations, and tests built incrementally, deployed weekly.
- Hand-off. Runbook, on-call playbook, monthly cost report template.
Deliverables
- Apache Airflow DAGs (or Dagster / Prefect, your call)
- PostgreSQL / BigQuery / Snowflake / MongoDB schemas
- dbt models (where applicable)
- Data quality tests (Great Expectations or similar)
- Grafana / DataDog monitoring
- Runbook + on-call playbook
Outcomes you can expect
- Pipeline uptime ≥ 99.9% (we've shipped pipelines processing 10M+ records daily at this SLA)
- Auditable lineage from raw source to consumed metric
- Monthly compute spend within ±10% of forecast
Pricing in US
Engagement size: $5,000–$60,000 USD per engagement.
Hourly rate: $95–$180 USD per hour.
How we contract: We can be engaged via direct contract, an Upwork enterprise plan, or as a 1099 contractor through your US LLC. We carry international wire and Wise USD receiving.
Timezone & availability
Operates 9am–6pm in your timezone (EST, CST, MST, PST) with overlap from Pakistan Standard Time
Tech stack
- Python, SQL
- Apache Airflow, Dagster, Prefect
- PostgreSQL, MongoDB, Redis, BigQuery, Snowflake, DuckDB
- Apache Kafka, Redpanda for streaming
- Docker, Kubernetes
- dbt for transformations
Relevant case studies
- Indonesia Livestock Operations Dashboard — A real-time monitoring and intelligence dashboard for managing livestock supply chain operations across Indonesia's provinces.
- Enterprise Data Pipeline & Analytics Engine — A production-grade data engineering pipeline processing 10M+ records daily with automated ETL workflows, real-time analytics, and comprehensive business intelligence reporting.
- Intelligent Web Scraping & Lead Enrichment Platform — A distributed web scraping system extracting and enriching business data from 50+ sources with anti-detection, proxy rotation, and intelligent rate limiting.
- Real-Time Analytics API with Flask & NoSQL — A high-performance Flask REST API for real-time event tracking and analytics, backed by MongoDB and Redis for sub-millisecond query responses.
Questions American buyers ask about data engineering
- How do you contract with American clients?
- We can be engaged via direct contract, an Upwork enterprise plan, or as a 1099 contractor through your US LLC. We carry international wire and Wise USD receiving.
- What about regulatory compliance in the United States?
- We work to HIPAA (healthcare PHI), SOC 2 Type II readiness for SaaS clients, CCPA / state privacy regimes, ITAR / EAR considerations only when applicable. Where audited compliance certifications are required, we partner with the right specialist firm and ship code that meets the technical controls.
- What's the timezone overlap?
- Operates 9am–6pm in your timezone (EST, CST, MST, PST) with overlap from Pakistan Standard Time
- What's a typical data engineering engagement size in US?
- $5,000–$60,000 USD per engagement, structured against fixed milestones. Hourly engagements are billed at $95–$180 USD per hour.
- Airflow vs. Dagster vs. Prefect — which do you recommend?
- Airflow when your team already knows it or when you need the largest operator ecosystem. Dagster when you want strong typing and asset-centric thinking. Prefect when you want the lightest setup. We don't push our preference; we match your team.
- Do you do streaming or only batch?
- Both. We've shipped Flask APIs handling 50K events/minute with Redis buffering and MongoDB aggregations under 100ms. For true streaming, Kafka + Flink or simpler alternatives like Redpanda + ksqlDB.
- What's your stance on data lakes vs. warehouses?
- Use a warehouse (PG/BigQuery/Snowflake) until you're spending more on warehouse storage than on compute. Then look at a lakehouse (Iceberg / Delta on S3). Don't lakehouse for the resume.
- Can you handle web scraping at scale?
- Yes. We've built distributed scraping platforms with anti-detection, proxy rotation, and 95%+ success rates on protected sites — handling 100K+ scraping jobs per day.
- Do you do data governance / cataloging?
- Light-touch by default (dbt docs + a catalog markdown checked into the repo). For larger orgs we integrate with Atlan, DataHub, or Amundsen.
- What about data quality testing?
- Great Expectations or dbt tests at every stage. Schema validations at ingest. Row-count and freshness checks at every materialization. Failed checks alert before downstream consumers see bad data.