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Data Engineering

Data Infrastructure That
Actually Scales.

ETL pipelines, data warehouses, lakehouse architecture, real-time streaming, and analytics infrastructure. Built by senior data engineers, deployed on your cloud, monitored from day one. We design, build, and maintain production data platforms that handle terabytes to petabytes without breaking your budget or your team's capacity.

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Senior Data Engineers GDPR & SOC2 Compliant 2 to 12 Week Delivery AWS / GCP / Azure
ETL Pipelines
Airflow · dbt · Fivetran · Custom
Data Warehouses
Snowflake · BigQuery · Redshift
Lakehouse Architecture
Databricks · Delta Lake · Iceberg
Real-Time Streaming
Kafka · Kinesis · Pub/Sub
50TB+
Data Processed Monthly
<5min
Pipeline Latency at p95
99.9%
Pipeline Uptime SLA
Capabilities

Every Data Capability, Production-Ready

We build data infrastructure for every layer of the stack, from ingestion and transformation to warehousing, governance, and BI integration. Each solution ships with monitoring, documentation, and a 90-day warranty.

ETL & ELT Pipeline Development
Batch and incremental pipelines connecting databases, APIs, file stores, and SaaS platforms to your warehouse. Built with Airflow, dbt, Dagster, or custom Python, with monitoring and alerting built in. We handle CDC from PostgreSQL and MySQL, API rate limiting, incremental load strategies, and backfill logic for historical data.
AirflowdbtDagsterFivetran
Data Warehouse & Lakehouse Architecture
Design and build cloud data warehouses (Snowflake, BigQuery, Redshift) and lakehouse platforms (Databricks, Delta Lake, Apache Iceberg) with star schema, medallion architecture, and cost optimization baked in. We optimize clustering keys, partitioning strategies, materialized views, and auto-scaling policies to keep query performance high and costs predictable.
SnowflakeBigQueryDatabricksDelta Lake
Real-Time Streaming & Event Processing
Event-driven data pipelines with Kafka, Kinesis, and Pub/Sub. Stream processing with Flink, Spark Streaming, and Kafka Streams for sub-second data freshness. Use cases include real-time dashboards, fraud detection alerts, inventory sync across systems, and operational analytics that need data within seconds, not hours.
KafkaFlinkSpark StreamingKinesis
Data Governance & Quality
Data cataloging, lineage tracking, quality checks, PII detection, and access controls. Great Expectations, dbt tests, DataHub, and custom observability dashboards. Every pipeline includes automated freshness checks, volume anomaly detection, schema validation, and a data quality scorecard that your team can monitor in real time.
Great ExpectationsDataHubdbt TestsSoda
BI & Analytics Infrastructure
Semantic layers, metric stores, and BI platform integration (Metabase, Looker, Power BI, Tableau). Pre-aggregated views, materialized tables, and performant dashboards at any scale. We design the analytics layer so your business users can self-serve without writing SQL or waiting on engineering.
LookerMetabasePower BIdbt Metrics
Cloud Migration & Modernization
Migrate on-premise data warehouses and legacy ETL to the cloud. Lift-and-shift or full re-architecture with minimal downtime and zero data loss guarantees. We handle schema mapping, data validation, cutover planning, and rollback procedures so your migration is low-risk and fully reversible.
AWSGCPAzureTerraform
Reverse ETL & Data Activation
Sync transformed, enriched data from your warehouse back into operational systems like Salesforce, HubSpot, Intercom, and your CRM. Keep your sales and support teams working with the freshest customer data without manual exports, CSV uploads, or stale records in your daily tools.
CensusHightouchCustomWebhooks
ML Feature Store & Data Pipelines
Build feature engineering pipelines and feature stores (Feast, Tecton) that serve consistent, fresh features to training and inference. We handle point-in-time correct joins, feature backfilling, online serving with low latency, and feature registry management for your ML team.
FeastMLflowSparkTecton
Data Observability & Monitoring
Complete observability stack covering pipeline freshness, row count anomalies, schema drift detection, and data quality dashboards. We set up alerting in PagerDuty, Slack, or email so your team knows the instant a pipeline breaks, before it impacts dashboards or downstream consumers.
Monte CarloSodaGreat ExpectationsGrafana
Architecture

Modern Data Stack Architecture

A production data platform is more than a database. It requires ingestion, transformation, orchestration, storage, governance, and reverse ETL, all with observability and cost management. Every architecture we build follows the principles of separation of compute and storage, idempotent pipelines, and infrastructure-as-code.

Medallion Architecture

Bronze (raw) > Silver (cleaned) > Gold (aggregated). The industry standard for lakehouse data platforms, built into Databricks and Delta Lake. Each layer enforces progressively stricter quality guarantees, so downstream consumers always query trusted data.

Infrastructure as Code

Every pipeline, warehouse, and permission set is defined in Terraform or Pulumi, version-controlled in Git, and deployed through CI/CD. No manual console clicks. Full reproducibility across dev, staging, and production environments.

01
Data Ingestion
Connect databases (PostgreSQL, MySQL, MongoDB), SaaS APIs (Stripe, HubSpot, Salesforce, Zendesk), file stores (S3, GCS, Azure Blob), and streaming sources (Kafka, Kinesis, webhooks). We handle schema inference, type casting, CDC from transactional databases, and backfill strategies for initial loads.
02
Orchestration & Transformation
Schedule and monitor pipelines with Airflow or Dagster. Transform raw data into clean, modeled tables with dbt, SQL, or Spark jobs. Every run logs completion status, row counts, runtime, and data quality metrics. Failed runs trigger automatic retries with exponential backoff and alert the on-call engineer.
03
Storage & Warehousing
Store processed data in Snowflake, BigQuery, Redshift, or Databricks Delta Lake. Optimized partitioning, clustering, and compression for query performance, plus auto-scaling and cost controls. We set up resource monitors and budget alerts so warehouse costs never exceed your targets.
04
Data Quality & Governance
Automated quality checks at every layer of the medallion architecture. Lineage tracking from source to dashboard, PII scanning with column-level classification, role-based access controls, and a searchable data catalog so your team can discover and trust the data they use.
05
BI, Reverse ETL & Activation
Connect to BI tools (Looker, Metabase, Power BI, Tableau), sync transformed data back to operational systems via reverse ETL, and power ML feature stores and analytics dashboards. This is where raw data turns into daily decisions across your organization.
Our Process

How We Deliver Data Infrastructure

A proven engagement model that moves from discovery to production in predictable sprints. Every phase includes documentation, knowledge transfer, and automated testing so your team can take over confidently.

01
Discovery & Audit

Map your data sources, volume, latency requirements, and team skills. Deliver a written architecture recommendation with platform comparison, cost estimate, and delivery timeline. You get a clear picture before any code is written.

02
Sprint Zero

Set up cloud infrastructure (Terraform), CI/CD pipelines, monitoring, and the first data source connection. Within two weeks you have a running pipeline with real data flowing into your warehouse, end to end.

03
Iterative Build

Two-week sprints adding sources, transformations, quality checks, and BI dashboards. You see working production code every sprint and can reprioritize as your needs evolve. Full test coverage on all transformations.

04
Handoff & Support

Full documentation, architecture diagrams, runbook, and knowledge transfer sessions. 90-day warranty on all code. Option to continue with a managed services retainer for monitoring, maintenance, and ongoing pipeline development.

Platform Comparison

Choosing the Right Data Platform

Each cloud data platform has strengths depending on your data volume, team expertise, latency needs, and existing cloud provider. Here is how the major platforms compare across the dimensions that matter most in production.

Snowflake
Cloud Native

Fully managed cloud warehouse with separate compute and storage. Instant scaling, zero maintenance, strong data sharing and marketplace ecosystem. Supports multi-cloud and cross-region replication with zero-copy cloning for dev/test environments.

Best for: Multi-cloud, data sharing, low-maintenance warehousing, teams that want to avoid infrastructure management entirely
Pricing model: Compute credits + storage, per-second billing, auto-suspend available
Databricks
Lakehouse

Unified analytics platform combining data lake and warehouse on Apache Spark. Delta Lake for ACID transactions and time travel, MLflow for ML lifecycle, Unity Catalog for governance, and collaborative notebooks for exploration. Best for teams running both analytics and machine learning on the same data.

Best for: Large-scale ETL, ML pipelines, data science teams, multi-modal workloads combining SQL and Python
Pricing model: DBU-based consumption, jobs compute, all-purpose compute, with spot instance support
BigQuery
Serverless

Google's serverless data warehouse with automatic scaling and columnar storage. Pay per query and per byte of storage, no cluster management, tightly integrated with GCP ecosystem including Dataflow, Pub/Sub, and Looker. Supports BI Engine for sub-second query acceleration.

Best for: GCP-native teams, ad-hoc analytics, massive datasets, organizations that want zero ops overhead
Pricing model: On-demand per query ($/TB scanned) or flat-rate slots with committed usage discounts
Amazon Redshift
AWS Native

AWS's petabyte-scale data warehouse with RA3 nodes that separate compute and storage. Redshift Spectrum queries data directly in S3 without loading. Integrated with Glue, EMR, Kinesis, and QuickSight. Concurrency scaling automatically adds capacity for spikes.

Best for: AWS-native teams, existing S3 data lakes, organizations already invested in the AWS ecosystem
Pricing model: Node-based hourly pricing, reserved instance discounts, concurrency scaling credits
Azure Synapse
Azure Native

Azure's integrated analytics service combining dedicated SQL pools, serverless SQL, and Apache Spark. Deep integration with Azure Data Factory, Power BI, and Microsoft 365 data. Pipelines, notebooks, and data flows in a single workspace.

Best for: Microsoft-centric organizations, Power BI-heavy teams, Azure-native enterprises with Active Directory
Pricing model: Dedicated pool (DWU) or serverless (per TB scanned), reserved capacity available
Apache Iceberg
Open Table

Open table format for huge analytic datasets with ACID transactions, time travel, schema evolution, and partition evolution. Works with Spark, Trino, Flink, Hive, and more. Avoids vendor lock-in by decoupling storage from compute. Ideal for multi-engine architectures.

Best for: Multi-engine architectures, avoiding vendor lock-in, teams that want open standards and flexibility
Pricing model: Open source (free), compute costs only from your processing engine
Real-World Use Cases

What Data Systems We Build for Businesses

From startups building their first warehouse to enterprises migrating petabyte-scale platforms to the cloud, these are the data infrastructure projects we deliver across industries.

Centralized Data Warehouse
Unify 10+ data sources including PostgreSQL, Stripe, HubSpot, and Google Analytics into a single Snowflake warehouse with dbt transformations and Looker dashboards. Your entire business reporting from one source of truth.
Real-Time Analytics Pipeline
Kafka ingesting clickstream and application events, Flink for stream processing, BigQuery for storage, with sub-second dashboards in Metabase. Perfect for product analytics, live operations monitoring, and fraud detection.
Databricks Lakehouse Platform
Medallion architecture with Delta Lake, MLflow for experiment tracking, Unity Catalog for governance, and AutoML for accelerated model development. Databricks SQL for BI and Power BI integration.
Data Migration to Cloud
Migrate on-premise SQL Server, Oracle, and legacy SSIS ETL to AWS Redshift with parallel run validation, zero data loss, and a fully reversible rollback plan. Cutover completed over a weekend.
Reverse ETL for Sales
Sync enriched customer data from the warehouse to Salesforce, HubSpot, and Intercom in real time. Sales teams get 360-degree customer profiles without manual data entry or CSV exports.
Data Governance Implementation
DataHub catalog, Great Expectations quality checks, Soda observability, and automated PII scanning with column-level classification. Full lineage tracking from source to dashboard for compliance and audit readiness.
ML Feature Pipeline
Feature engineering pipeline using Spark and Feast, serving training features for batch ML models and online features for real-time inference via a low-latency API. Point-in-time correct joins for accurate historical backtesting.
Financial Reporting Platform
Multi-entity data consolidation across subsidiaries into a unified BigQuery warehouse with GAAP-compliant transformations, audit trails, and automated regulatory reporting dashboards for the finance team.
Data Platform for E-Commerce
Ingest Shopify, Amazon, Google Ads, Facebook Ads, and warehouse inventory data into a unified analytics model. Real-time inventory dashboards, customer LTV models, and marketing attribution reports in one platform.
Engagement Models

Pricing & Engagement That Fits Your Needs

We offer two engagement models. Both include the same senior engineering talent, same 90-day warranty, and same commitment to production-grade code. Choose the model that matches your data maturity and team capacity.

Project-Based (Fixed Price)

Best for well-defined scopes with clear requirements and known data sources. You get a fixed price, fixed timeline, and a dedicated senior engineer leading the build.

  • Fixed scope and budget, no surprises
  • Senior engineer dedicated to your project
  • Bi-weekly progress demos
  • Documentation and knowledge transfer included
  • 90-day warranty on all delivered code
Typical range: $5,000 to $50,000+ depending on scope and data volume
Dedicated Engineer (Monthly Retainer)

Best for ongoing data engineering needs, evolving requirements, or teams that need continuous pipeline development, monitoring, and maintenance without hiring a full-time data engineer.

  • Dedicated senior data engineer embedded in your team
  • Slack-based communication, daily standups
  • Flexible scope, reprioritize each sprint
  • Includes monitoring, maintenance, and cost optimization
  • No long-term contract, month-to-month available
Typical range: $4,000 to $12,000 per month per engineer

Not sure which model fits your situation? Book a free 45-minute data architecture audit. We will review your data stack, recommend the right engagement model, and give you a realistic estimate. No commitment required.

Book Free Audit
Start Your Data Project

Book a Free Data Architecture Audit

Tell us about your data sources, volume, and goals. A senior data engineer will review your current setup, recommend the right architecture, and give you a realistic delivery estimate, free, no obligation. We typically respond within 24 hours with a preliminary assessment.

45-Minute Technical Call
With a senior data engineer, not a sales rep. Expect a deep discussion about your data volume, sources, transformation logic, latency requirements, and team capabilities.
Architecture Recommendation
Platform choice (Snowflake, BigQuery, Databricks, Redshift, or a combination), pipeline design, data model sketch, and a cost estimate for infrastructure and build effort.
Realistic Delivery Estimate
Timeline by phase (sprint zero, first pipeline, full platform), team size recommendation, cost ballpark, and a clear statement of what is in scope and what is out of scope.
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What Happens Next
01
Data Audit, 45-min session with a senior data engineer to map your sources, volume, latency requirements, team skills, and business goals
02
Architecture Plan, Platform recommendation, pipeline design, cost estimate for infrastructure and build, delivery timeline, and engagement model recommendation delivered within 48 hours
03
First Pipeline in 2 Weeks, Sprint zero kicks off within days of approval. First data pipeline ingesting and transforming your data into the warehouse within 14 calendar days
Our Guarantee

Every data pipeline ships with a 90-day warranty. If anything breaks due to our code, we fix it at no cost, no questions asked. We also include full documentation, architecture diagrams, and a knowledge transfer session so your team can operate and extend the platform independently.

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Talk to a Data Engineer
// free 45-min call · no commitment
FAQ

Common Data Engineering Questions

Everything you need to know. Can't find what you're looking for? Talk to us

Data engineering is the practice of designing, building, and maintaining systems that collect, store, transform, and make data available for analysis and machine learning. It covers ETL pipelines, data warehouses, data lakes, streaming infrastructure, and the orchestration that keeps data fresh and reliable at scale. Without data engineering, raw data stays locked in source systems, unusable for analytics or AI.
A data warehouse stores structured, processed data optimized for SQL queries and BI tools. A data lake stores raw data in its native format. A lakehouse (like Databricks) combines both: raw storage with warehouse-style performance, ACID transactions, and direct ML integration, eliminating the need to maintain separate systems. The lakehouse is increasingly the default choice for new data platforms.
Databricks is a unified data analytics platform built on Apache Spark that combines data engineering, data science, and machine learning on a single lakehouse architecture. Use it when you need to process large-scale data, run complex transformations, and build ML models on the same infrastructure without managing Spark clusters. It is especially valuable for teams that need Python and SQL together on the same platform.
A batch ETL pipeline connecting a few sources to a warehouse typically takes 2 to 4 weeks. Real-time streaming pipelines with Kafka and complex transformations require 6 to 12 weeks. A full data platform with lakehouse architecture, orchestration, and observability takes 8 to 16 weeks depending on scope. Sprint zero, where the first end-to-end pipeline is delivered, takes less than 2 weeks.
Yes. We build on all three major clouds. AWS (Redshift, Glue, EMR, Kinesis, S3), GCP (BigQuery, Dataflow, Pub/Sub, Composer), and Azure (Synapse, Data Factory, Databricks, Event Hub). We recommend the platform that best fits your data volume, latency requirements, team skills, and existing infrastructure. All infrastructure is deployed as code using Terraform or Pulumi.
Cost depends on data volume, source complexity, transformation requirements, and infrastructure. A simple batch pipeline is a smaller build than a real-time streaming platform with complex transformations. Share your data sources and requirements and we will scope it and quote a fixed price. Project engagements typically range from $5,000 to $50,000+. Dedicated engineer retainers start at $4,000 per month.
The medallion architecture is a data design pattern with three layers: Bronze (raw ingested data, unchanged from source), Silver (cleaned, deduplicated, and validated data), and Gold (aggregated business-level metrics and models). It is the industry standard for lakehouse platforms like Databricks and Delta Lake. Each layer enforces progressively stricter quality guarantees, ensuring downstream consumers always query trusted data.
Reverse ETL is the process of moving transformed, enriched data from your data warehouse back into operational systems like Salesforce, HubSpot, or your CRM. It ensures your sales and support teams always work with the freshest customer data without manual exports or duplicate records. In practice, it means a sales rep sees the customer's latest support tickets, product usage, and payment history directly in Salesforce.
ETL (Extract, Transform, Load) transforms data before loading it into the warehouse, often using a separate transformation server. ELT (Extract, Load, Transform) loads raw data first and transforms it inside the warehouse using the warehouse's own compute power. ELT is preferred with modern cloud warehouses like Snowflake and BigQuery because it is faster, more scalable, and more flexible. dbt is the most popular tool for ELT transformations.
Yes. Every pipeline ships with a 90-day warranty for defects in our code. We also offer ongoing managed services retainers covering monitoring, maintenance, pipeline updates, infrastructure cost optimization, and new source onboarding. You can scale support up or down as your data needs evolve, and you can cancel with 30 days notice. No long-term lock-in.
Data observability is the practice of monitoring data pipelines for freshness, volume, schema changes, and quality in real time. Tools like Great Expectations, Soda, and Monte Carlo detect issues before they reach dashboards or downstream consumers. Without observability, broken pipelines can silently deliver bad data for days or weeks, eroding trust in your analytics and driving bad business decisions.
Yes. We handle full migrations from on-premise SQL Server, Oracle, Teradata, and legacy ETL tools (SSIS, Informatica) to cloud warehouses on AWS, GCP, or Azure. Our approach minimizes downtime through parallel runs, validates data completeness after each migration batch, and includes a fully reversible rollback plan. Zero data loss is guaranteed throughout the migration process.
Stop Letting Your Data Sit in Silos

You have valuable data across databases, APIs, and files. Let's build a pipeline that brings it all together, reliably, at scale, so you can actually use it. Book a free audit and get a clear plan within 48 hours.

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