DATA ENGINEERING SERVICES

From Raw Data
To Decision-Ready,
In Weeks.

We design and build modern data platforms — Snowflake, Databricks, Apache Spark — that make your data fast, clean, and AI-ready. No more "the data team needs 6 months."

IBM Certified
Apache Spark Dev
Snowflake Cert
SnowPro Core
Databricks Cert
Lakehouse Eng

Discovery call. Architecture review. Honest scope, honest quote.

Data Pipeline · Live
Streaming

Raw Data Sources

Salesforce · SAP · Mobile Apps · IoT · APIs

Ingestion Layer

Apache Spark · Kafka · Airbyte

Lakehouse / Warehouse

Snowflake · Databricks · Delta Lake

Transformation

dbt · Spark Jobs · Airflow

Decision-Ready Data

BI · ML · APIs · Real-Time Apps

What We Build, Specifically

Real engineering deliverables — not vague consulting hours.

Data Lakehouses

Snowflake or Databricks Lakehouse architecture, designed for both BI and ML workloads. Optimized for cost and query speed.

SnowflakeDatabricksDelta Lake

ETL/ELT Pipelines

Production-grade data pipelines with Apache Spark, dbt, and Airflow. Reliable. Monitored. Alerted on failure.

SparkdbtAirflow

Real-Time Streaming

Kafka and Spark Streaming for sub-second data freshness — for fraud detection, real-time dashboards, IoT.

KafkaSpark StreamingFlink

Cloud Data Warehouses

Snowflake, BigQuery, Redshift — designed and tuned for cost efficiency. We've seen warehouse bills cut 40%+ with proper modeling.

SnowflakeBigQueryRedshift

Data Quality & Observability

dbt tests, Great Expectations, Monte Carlo. Catch bad data before it breaks reports — not after.

dbt testsGreat ExpectationsMonte Carlo

Legacy DB Migrations

Oracle / Teradata / SQL Server to cloud lakehouse — without breaking existing reports. Parallel running until cutover.

OracleTeradataSQL Server
CERTIFIED & EXPERIENCED

The Stack We Build On

Production-grade, mainstream, hireable-anywhere stack. No exotic frameworks. No proprietary lock-in.

Data Storage & Warehousing

❄
Snowflake
BI & analytics workloads
DB
Databricks
ML + lakehouse unified
Δ
Delta Lake
ACID on the lake
S3
Object Storage
Raw + archive layers

Processing & Pipelines

⚡
Apache Spark
Distributed processing
dbt
dbt
SQL transformations
AF
Airflow
Workflow orchestration
K
Kafka
Real-time streaming

Cloud Platforms

aws
AWS
Most common. Mature ecosystem.
GCP
Google Cloud
BigQuery-led analytics
⊞
Azure
Microsoft-shop preferred
OP
On-Premise
For regulated environments

Certifications We Hold

Snowflake
SnowPro Core
Databricks
Lakehouse Engineer
IBM
Apache Spark Developer
AWS
Solutions Architect

How We Approach Every Engagement

Repeatable. Phased. Visible progress every two weeks. No 6-month black boxes.

1

Discovery & Audit

Week 1

We map your sources, current pipelines, pain points. Document what exists. Identify high-value targets first.

  • Source system inventory & data flow mapping
  • Current pipeline audit (what works, what doesn't)
  • Pain-point interviews with data consumers
  • Output: written delivery plan with prioritization
2

Architecture Design

Week 2

We design the target architecture (lakehouse, warehouse, pipelines, monitoring). Sign-off before any building.

  • Lakehouse / warehouse choice with cost modeling
  • Pipeline architecture (batch vs streaming)
  • Data governance & security model
  • Sign-off review with your team before build
3

Foundation Build

Weeks 3-6

Cloud infrastructure, base data models, ingestion of top 5 sources. First end-to-end demo by week 6.

  • Cloud account setup, security, networking
  • Base data models in Snowflake or Databricks
  • Ingestion pipelines for first 5 priority sources
  • End-of-week demos every Friday
4

Production Pipelines

Weeks 7-12

Full pipeline coverage, data quality tests, monitoring, alerting. Existing reports start running on the new platform.

  • Remaining sources ingested
  • Transformations in dbt with documented tests
  • Monitoring & alerting in place
  • Parallel-run with old system for validation
5

Cutover & Knowledge Transfer

Weeks 13-16

Old systems decommissioned. Your team trained. Documentation handed over. Optional 30-day post-launch support.

  • Cutover from old systems
  • 5-day knowledge transfer to your data team
  • Runbooks & architecture documentation
  • Optional 30-day post-launch support window

Common Engagements We Take On

If your situation looks like one of these — we can help.

ENTERPRISE

Legacy Oracle Modernization

The Trigger

You have a 10+ year Oracle data warehouse that's slow, expensive to license, and impossible to add ML to.

What We Build
  • Snowflake migration with parallel-run validation
  • dbt-based transformations replacing legacy stored procs
  • New BI/ML access layer on top
GROWTH STAGE

Real-Time Analytics Layer

The Trigger

Your dashboards refresh every 24 hours. Your business needs to see what's happening NOW.

What We Build
  • Kafka streaming infrastructure
  • Spark Structured Streaming aggregation
  • Real-time dashboards reflecting events under 30s
AI TRANSFORMATION

AI/ML-Ready Data

The Trigger

Your data scientists spend 80% of their time cleaning data. Stop the bleeding.

What We Build
  • Feature stores for reusable ML features
  • Training data pipelines with versioning
  • Vector embeddings infrastructure for AI/RAG
GROWING BUSINESS

Multi-System Data Unification

The Trigger

You have data in 8 SaaS tools. Reporting takes a week to assemble manually.

What We Build
  • Unified data lake with automated ingestion
  • Single source-of-truth dimensional model
  • Self-service BI on top

Is This The Right Fit For You?

We're upfront about who this service serves best — and who should look elsewhere.

Great Fit

Modern Data Infrastructure as Strategic Capability

For organizations ready to invest in their data platform as a long-term advantage.

  • Mid-to-large enterprises with multiple data sources
  • Companies on legacy Oracle/Teradata/SQL Server feeling licensing pain
  • Growth-stage startups outgrowing their first analytics setup
  • Organizations preparing data infrastructure for AI/ML initiatives
  • Teams that want their data platform owned, not rented
Not the Right Fit

When to Look Elsewhere

Honest about when this isn't the right service.

  • Small teams with under 100GB of data — Postgres + a BI tool is enough
  • Companies needing a single dashboard fast — see our Power BI Reports service
  • Organizations without budget for $30K+ data engineering work
  • Teams looking for staff augmentation ("data engineer for hire") arrangements

Common Questions

The questions we get on every data engineering discovery call.

Got Data That Should Be Doing More? Let's Talk.

30-min discovery call. We'll review your current architecture, identify the biggest leverage points, and quote upfront.

We respond within 24 hours. Zero spam. Zero obligation.