Data Engineering & MLOps | HighQ-Labs

HighQ-Labs Data Engineering & MLOps

Turn raw data into models that actually ship.

We build the pipelines, infrastructure, and MLOps practices that take data from scattered sources to production-grade machine learning — reliably, and on a schedule you can trust.

Talk to Our Data Team → See Our Approach

Data Pipelines MLOps Real-Time Analytics

CRM Data

App Database

IoT / Logs

Data Warehouse

ML Models

🔄

Automated Data Pipelines

No manual data wrangling

📊

Real-Time Model Monitoring

Drift caught before it costs you

10+

Yrs Combined Engineering Experience

Data & ML Capabilities

Layer Tech Stack

Stage Delivery Roadmap

What we build

Every layer of the data-to-model journey

Six capabilities, configured the way your team would actually define them.

Data Pipeline Engineering

pipeline:
  source: postgres
  transform: dbt
  destination: snowflake
  schedule: “*/15 * * * *”

Data Warehousing & Lakehouse

lakehouse:
  format: delta
  engine: databricks
  partitioning: date
  retention: 365d

Real-Time Streaming

stream:
  broker: kafka
  topic: events.raw
  consumer: flink
  latency_target: <500ms

Feature Engineering & Feature Stores

feature_store:
  registry: feast
  online: redis
  offline: bigquery
  ttl: 30d

Model Training & Deployment

model:
  framework: pytorch
  registry: mlflow
  serving: sagemaker
  rollout: canary

Model Monitoring & Drift Detection

monitor:
  metric: data_drift
  threshold: 0.15
  alert: slack
  retrain: auto

Why MLOps isn’t just DevOps

Models need a different pipeline than code does

Software ships once code passes tests. Models need to keep being re-validated against data that keeps changing.

Traditional Software Delivery

Code

Build

Test

Deploy

ML Model Delivery (MLOps)

Data

Train

Validate

Deploy

Monitor

Retrain

Retrain feeds back into Train — this loop never really ends.

Technology stack

Tools chosen per layer, not one-size-fits-all

Ingestion & ETL

KafkaAirbyteFivetran

Storage & Warehousing

SnowflakeBigQueryDatabricks

Orchestration

AirflowDagsterPrefect

ML Platform

MLflowSageMakerVertex AI

Governance built in

Data you can trust, not just data you have

Data Governance & Lineage Tracking

GDPR & Data Privacy Compliance

Automated Data Quality Checks

Role-Based Data Access Control

Delivery roadmap

From scattered data to a model in production

Assess Data Landscape

Audit existing sources, quality issues, and gaps before building anything.

Build Pipelines

Stand up reliable ingestion, transformation, and storage layers.

Train & Validate

Develop and rigorously test models against real business scenarios.

Deploy & Monitor

Ship to production with continuous monitoring and automated retraining triggers.

Assess Data Landscape

Audit existing sources, quality issues, and gaps before building anything.

Build Pipelines

Stand up reliable ingestion, transformation, and storage layers.

Train & Validate

Develop and rigorously test models against real business scenarios.

Deploy & Monitor

Ship to production with continuous monitoring and automated retraining triggers.

📊 Reliable Pipelines

🤖 Models in Production

🔒 Governed Data

Drowning in data but still flying blind on decisions?

Let’s find out what your data is actually capable of telling you.

Talk To Experts →