Data Pipelines That Run. Reliably. At Scale.
Your data team is drowning in maintenance. We build pipelines, analytics backends, and ML infrastructure that run reliably — so your team can focus on insights, not firefighting.
We design and build the data infrastructure that powers your analytics, machine learning, and business intelligence. From raw data ingestion to production-grade feature stores, our data engineers build systems that deliver trustworthy data to every team that needs it.
Book a CallWhen your data infrastructure breaks trust
Your pipelines break every Monday morning
Airflow DAGs fail silently. Data arrives late, incomplete, or duplicated. Your data team starts every week debugging pipelines instead of delivering insights. The "quick fix" list has been growing for months.
Nobody trusts the numbers
Marketing's dashboard shows different numbers than Finance's spreadsheet. Your CEO asks a simple question and gets three different answers. Data quality issues have eroded confidence in every report.
Your data warehouse is a swamp
Thousands of tables, no documentation, no ownership. Queries that should take seconds take minutes. Your analysts spend 80% of their time finding and cleaning data, and 20% actually analyzing it.
You can't support ML with your current infrastructure
Your data scientists want to train models, but there's no feature store, no experiment tracking, and no way to get production data into training pipelines without custom scripts that break monthly.
What we build
Data infrastructure that's reliable, documented, and actually maintained.
Data Pipeline Architecture
ETL/ELT pipelines using Airflow, Prefect, or dbt that run reliably at scale. Incremental processing, idempotent operations, and proper error handling — not fragile cron jobs held together with hope.
Data Warehouse & Lakehouse
Modern analytical infrastructure on Snowflake, BigQuery, Databricks, or Redshift. Dimensional modeling, slowly changing dimensions, and query optimization that keeps your analysts productive.
Real-Time Data Streaming
Kafka-based streaming architectures for real-time analytics, CDC pipelines, and event-driven data products. Sub-second data freshness for use cases where batch processing isn't fast enough.
Data Quality & Observability
Automated data quality checks, anomaly detection, lineage tracking, and alerting. Great Expectations, Monte Carlo, or custom validation frameworks that catch problems before your stakeholders do.
ML Data Infrastructure
Feature stores, training data pipelines, experiment tracking, and model registries. The infrastructure your data scientists need to go from notebooks to production without reinventing the wheel.
Analytics Engineering
dbt models, semantic layers, and self-service analytics infrastructure. We transform raw data into clean, tested, documented datasets that business users can query confidently.
Our data engineering stack
Orchestration
Processing
Storage
Streaming
Quality
ML Infra
How we fix your data infrastructure
Data Audit
We map your data sources, pipelines, and consumers. We identify reliability issues, quality gaps, and architectural debt. You get a prioritized action plan with quick wins and long-term improvements.
Foundation First
We fix the fundamentals: reliable ingestion, proper orchestration, basic quality checks, and documentation. This phase alone often eliminates 80% of your pipeline failures.
Scale & Optimize
Once the foundation is solid, we build for growth: real-time pipelines, advanced transformations, feature stores, and self-service analytics layers.
Enable Your Team
We document everything, train your team, and establish data engineering best practices. The goal is for your team to maintain and extend the infrastructure independently.
Why Pletava
Engineers who own reliability
We don't build pipelines and walk away. We build monitoring, alerting, and runbooks so failures are caught and resolved automatically — or at least quickly.
Business-aware data modeling
We don't just move data. We understand your business context, model data for your actual analytical needs, and build semantic layers that business users can navigate without SQL knowledge.
Modern stack, pragmatic choices
We use best-in-class tools but don't over-engineer. If a simple dbt project solves your problem, we won't build a Spark cluster. Right tool, right scale.
Frequently Asked Questions
Can't find what you're looking for? Book a call and we'll answer everything.
Book a CallShould we use Snowflake, BigQuery, or Databricks?
It depends on your workload, team skills, and existing cloud provider. We help you evaluate options based on your actual requirements — not vendor marketing. We're cloud and tool agnostic.
How long does it take to fix our pipeline reliability?
Quick wins (monitoring, alerting, critical bug fixes) typically take 2–4 weeks. A proper foundation (reliable orchestration, quality checks, documentation) is usually a 2–3 month effort.
Can you work alongside our existing data team?
That's our preferred model. We embed with your team, work in your repositories, and follow your processes. Knowledge transfer happens naturally through daily collaboration, not a formal handoff.
Do you handle data governance and compliance?
Yes. We implement access controls, data classification, PII handling, retention policies, and audit trails. We've worked with GDPR, HIPAA, SOC 2, and PCI DSS requirements.
Your data should drive decisions, not debugging sessions.
Talk to data engineers who build infrastructure that lasts.
Thrilled to meet you!
Let's talk possibilities