Category C · 5 services

Data Engineering & Analytics

The pipelines, models, and definitions that make a number mean the same thing twice.

Analytics fails on trust long before it fails on technology. Two dashboards disagree, nobody can say which is right, and the meeting reverts to opinion. We build data platforms for freshness and lineage rather than volume alone: one place for metric definitions, column-level lineage end to end, and contract tests that fail a pipeline before a dashboard reports a wrong number.

What we build

C.1

Data Platforms & Lakehouse Engineering

Lakehouse storage with schema evolution that does not break readers

Data platforms built for freshness and lineage. Lakehouse storage on Iceberg or Delta with schema evolution that does not break downstream readers, change-data-capture ingestion from operational systems without dual writes, batch and streaming transformation in one framework, columnar and time-series serving layers, cost-aware partitioning and compaction, and access control that follows the data rather than the tool.

Apache Iceberg
Delta Lake
Spark
dbt
Parquet
CDC

What we build

C.2

Business Intelligence & Reporting

One definition of a metric, consumed by every tool downstream

Reporting that a leadership team can actually argue from. A semantic layer holding metric and dimension definitions in one place, dashboards designed for a decision rather than for a screenshot, self-service models that do not require a data team for every question, scheduled and event-driven distribution, and a certification process so people know which report is authoritative.

Power BI
Looker
Metabase
Semantic layer
dbt metrics

What we build

C.3

Data Integration, ETL & Migration

Moving data between systems, and proving nothing was lost on the way

Pipelines and migrations that can be verified rather than trusted. Ingestion from databases, applications, files, and APIs, transformation with tests at every stage, orchestration with retries and idempotent reruns, historical backfill without downtime, and migration runbooks with reconciliation reports that prove source and target agree before anything is switched off.

Airflow
Dagster
Debezium
Fivetran
Reconciliation
Backfill

What we build

C.4

Real-Time & Streaming Analytics

Sub-second freshness where a stale number is a wrong decision

Streaming architectures for the decisions that cannot wait for tonight's batch. Event ingestion at scale over Kafka or Pulsar, stream processing in Flink or Spark with exactly-once sinks, windowing and late-arrival handling that reflects how the business actually thinks about time, real-time aggregation into serving stores, and operational dashboards that update without a refresh button.

Kafka
Flink
Pulsar
ClickHouse
Exactly-once
Event time

What we build

C.5

Data Governance, Quality & Privacy

Lineage, quality gates, and a catalogue people actually use

Governance that makes data usable rather than merely documented. A catalogue populated automatically from the platform, column-level lineage end to end, data quality tests that fail a pipeline before a dashboard lies, ownership assigned to a person rather than a department, classification and masking for personal and regulated data, retention enforced by the platform, and subject access requests answerable without a manual search.

Data catalogue
Column-level lineage
GDPR
DPDP Act
Masking
Retention policy

What we build

How it connects

Inside data & analytics.

The systems in this area, and what each one hands to the next.

Data Platforms

Data platforms built for freshness and lineage.

What we build

Business Intelligence

Reporting that a leadership team can actually argue from.

What we build

Integration & Migration

Pipelines and migrations that can be verified rather than trusted.

What we build

Streaming Analytics

Streaming architectures for the decisions that cannot wait for tonight's batch.

What we build

Data Governance

Governance that makes data usable rather than merely documented.

What we build

Bring us the whole problem.

Tell us where the work is stuck, whether that is a model that never reached production, an application nobody can change, a data platform nobody trusts, or a plant the business cannot see. An engineer replies with a first read, not a sales deck.