Pure Technology
Data Engineering

Data pipelines you can bet a quarterly report on.

Modern data engineering — warehouses, lakes, streams, and the governance layer — built so analysts and AI teams stop arguing about which number is real.

60+
Data platforms shipped
<5 min
Avg. SLA freshness
100%
Lineage coverage
dbt-native
By default

Built for teams that need this to just work.

Who this is for

  • Companies whose dashboards disagree depending on who opens them.
  • Analytics teams blocked on raw access while data engineering rewrites pipelines.
  • AI teams needing a feature store and clean training data.
  • CFOs needing audited, reconciled data for board reporting and compliance.
Technology Expertise

Technologies Your Teams Already Trust.

We build teams around modern engineering ecosystems ensuring every developer is aligned with your stack, workflows, and delivery standards.

L5

ETL Engineers

Batch & Stream ETL

Apache SparkFlinkKafka
L5

Pipeline Engineers

Orchestration

AirflowPrefectDagster
L6

Streaming Eng.

Real-time Streaming

KafkaKinesisPub/Sub
L4

Integration Eng.

Data Integration

FivetranAirbytedbt
What we deliver

A full-stack capability - not a job title.

Every engagement is led by senior practitioners. You meet them in the pitch; they ship the work.

Modern warehouse

Snowflake, BigQuery, Databricks, Redshift — architected for cost, performance, and governance.

ELT & ingestion

Fivetran, Airbyte, custom CDC, and event pipelines — observable, idempotent, and replayable.

dbt-first modeling

Layered models, tests, exposures, and semantic layer — analysts can self-serve safely.

Streaming pipelines

Kafka, Flink, Materialize, and Snowpipe — for use cases where seconds matter.

Governance & lineage

Catalog, lineage, PII tagging, and access controls — wired into the warehouse, not bolted on.

Feature stores for ML

Tecton, Feast, or warehouse-native — supporting both batch training and real-time inference.

Outcomes that matter

Numbers from real engagements.

43%
Warehouse spend cut

Workload tuning + warehouse rightsizing on a Snowflake bill running 7 figures.

2 min
Pipeline freshness

Reduced from a 6-hour batch to a sub-2-minute CDC stream for a fintech.

100%
Lineage coverage

Every model traced from source to dashboard — default on every engagement.

Case studies

Recent work, anonymised where it has to be.

Numbers are real, names are sometimes changed at the client's request.

Retail Analytics
Retail
Case study

Retail Analytics

Challenge — Nightly batch jobs missed SLAs during peak season; business trusted Excel exports more than the warehouse.

What we did — dbt + Airflow lakehouse with data contracts and freshness alerts. Pipeline SLAs met 99.2% over peak quarter.

99.2%
SLA met
−70%
Job failures
4 hr
Freshness SLO
Insurer
Insurance
Case study

Insurer

Challenge — Claims data sat in silos — actuarial models ran on stale extracts with no lineage for regulators.

What we did — Unified bronze-silver-gold model with lineage in OpenLineage. Regulatory submissions cut from 3 weeks to 4 days.

4 days
Submission cycle
100%
Lineage tracked
1
Source of truth
FAQ

The questions we hear most.

Do we need to switch warehouses?+

Usually not — we work with what you have, and migrate only when the ROI is clear.

Can you support our analytics team?+

Yes — we run enablement, code reviews, and embedded analytics engineering pods.

How do you handle PII and compliance?+

Column-level masking, row-level security, and tagged lineage — designed for SOC 2, GDPR, and HIPAA.

Related services

Most engagements span more than one practice.

Free consultation

Turn Your Vision
Into Reality

What's Next?

  1. 1

    Drop your requirement and our expert will analyze further

  2. 2

    Outlining it, we will build roadmap and connect with you

  3. 3

    Further, finalize the approach and begin implementation

Your Tech-Powered Success is on Us

Ready to scope this in detail?

A 30-minute call with a senior engineer. No sales theatre — just a real assessment of fit, scope, and timeline.