Data Engineering

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.

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 & timeline

Pipeline builds $10K–$40K USD. Ongoing data-engineering partner $4K–$12K USD/month.

First DAG in production in 2–3 weeks; full migration typically 6–12 weeks.

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.

Frequently asked questions about data engineering

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.