Haink builds data platforms that turn scattered data into decisions — modeling, warehousing, lakehouse architectures, reliable pipelines, self-service BI and governance. A senior-only team delivers analytics that scale with your business and feed your AI workloads.
Dimensional and domain models on Snowflake, BigQuery, Redshift or Postgres, built for performance and clarity.
Unified lakehouse on Databricks/Delta or open table formats for analytics and ML on one platform.
ELT with dbt and orchestration (Airflow/Dagster) — tested, observable and reproducible.
Self-service BI in Power BI, Looker or Metabase that stakeholders actually use.
Cataloging, data quality, lineage and access control so data is trusted and compliant.
Feature pipelines, embeddings stores and curated datasets that power ML and LLM applications.
Typical stack:
Production work delivered by our engineering team. Client names withheld under NDA; sectors shown to indicate context. See full case studies →
Data modeling over population density and transport accessibility, with genetic-algorithm optimization and a digital-twin simulation to validate the plan before anything moved.
A real-time analytics and ML moderation pipeline flagging violations at high precision and cutting manual review.
It depends on your cloud, scale and budget — we work across Snowflake, BigQuery, Databricks, Redshift and Postgres, and recommend based on your workloads rather than a default.
A lakehouse unifies analytics and ML on one platform and is often the best fit; we'll recommend the simplest architecture that meets your needs.
Cataloging, automated data-quality tests, lineage tracking and access controls built into the pipelines from the start.
Yes — we build feature pipelines, curated datasets and embeddings stores so the same platform powers BI and AI.
Yes. We deploy in your cloud account or on-premises, including for data-residency and compliance requirements.
Let's shape a clear plan with milestones, architecture options and an implementation roadmap — with right-sized GPU hardware if AI workloads are involved.