Hire a Data Engineer who has actually shipped to production.
Most data engineers can build a pipeline in a Jupyter notebook. Few can run one in production for two years without it silently breaking. Our data engineers come from the bench that built the data infrastructure behind Clust — real-time ingestion, schema validation, LLM-embedded pipelines — and from production blockchain delivery on the ICICB-managed Atari ecosystem.
Senior engineers, vetted against production-grade work — not LinkedIn keywords.
Production track record across regulated industries — fintech, blockchain platforms, and large-volume data infrastructure.
Bench-vetted by senior data architects who actually run production systems, not by HR keyword filters.
Strong on data contracts, schema governance, and the operational discipline that separates working pipelines from reliable ones.
Comfortable across the modern stack — Snowflake, Databricks, Kafka, Airflow, dbt, PySpark — and honest about which one fits which workload.
The depth our data engineers bring to your team.
Pipelines
- Apache Airflow
- Dagster
- dbt
- Prefect
- Schema validation
- Data contracts
Streaming
- Apache Kafka
- Kafka Streams
- PySpark Structured Streaming
- Flink
- CDC with Debezium
- Sub-second latency event pipelines
Warehouses & Lakes
- Snowflake
- Databricks
- BigQuery
- Redshift
- Delta / Iceberg / Hudi
- Lakehouse architecture
AI-Embedded Pipelines
- LLM classification pipelines
- Embedding generation
- Vector indexing
- Anomaly detection
- Semantic enrichment
Cloud
- AWS data services
- GCP data services
- Azure Synapse
- IaC with Terraform
- Cost optimization
BI & Activation
- Power BI
- Tableau
- Looker
- Reverse ETL
- Operational analytics
From request to engineer-on-keys, fast.
Brief Call
A 30-minute call to understand your stack, your problem, and the seniority you actually need (versus what the JD says).
Engineer Match
We propose 1–2 engineers from the bench who fit the brief — with portfolio links to real shipped work, not pitch slides.
Technical Interview
You interview the engineer directly. Pass or fail is your call. We re-match if needed at no cost.
Onboard
Engineer joins your team within 1 week of offer. Monthly retainer, no hidden fees, replacement guaranteed.
Real-time data infrastructure with embedded LLM processing — anchored in more than a decade of production delivery.
Clust GPU cloud platform. AlgoCoder engineered the data infrastructure end-to-end — high-volume real-time ingestion, ETL orchestration, schema validation, data quality checks — plus LLMs embedded directly inside production data pipelines for classification, semantic enrichment, unstructured-to-structured conversion, and anomaly detection at platform volume.
Operational discipline carried over from production blockchain delivery on the ICICB-managed Atari blockchain ecosystem — the difference between a working pipeline and one you can actually rely on is operational rigour, not framework choice.
- Clust end-to-end data infrastructure — high-volume real-time ingestion, ETL orchestration, schema validation, data quality.
- LLMs embedded directly inside Clust's production data pipelines for classification, semantic enrichment, structuring, and anomaly detection at platform volume.
- Real-time streaming with Kafka and Airflow, data lakes, governance frameworks, Snowflake and Databricks platforms.
- Schema contracts and lineage baked in from commit one — not retrofitted after the data swamp arrives.