Data Pipeline Engineering
Get the right data to the right place, on time - reliable pipelines that feed your analytics, ML models, and operational dashboards.
Trusted by companies that ship production software on deadline
Everything you need, nothing you don't
We scope each engagement precisely so you get senior-level work on the capabilities that matter.
ETL/ELT workflows
Extract from sources, transform with business logic, and load into warehouses - with idempotent jobs that recover from failures.
Real-time streaming
Process events as they happen with Kafka and stream processors - for live dashboards, alerts, and ML feature stores.
Data warehousing
Modeled schemas in Snowflake, BigQuery, or Redshift - optimized for analytics queries and BI tool performance.
Data quality checks
Automated validation for completeness, freshness, and schema drift - with alerts before bad data reaches downstream consumers.
Schema evolution
Handle changing source schemas without breaking pipelines - backward-compatible migrations and versioning built in.
Batch processing
Scheduled jobs for nightly aggregations, report generation, and model training - with dependency orchestration and retries.
From scattered sources to trusted data
We audit your data landscape, design pipeline architecture, and build pipelines that your analytics and ML teams can depend on.
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Week 1: Data audit & architecture
We map your sources, define SLAs, and design the pipeline topology - batch, streaming, or hybrid based on your needs.
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Weeks 2-8: Build & validate
Pipelines ship incrementally by domain - each sprint delivers tested, monitored jobs with data quality checks in place.
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Launch: Monitor & document
We add observability, alerting, and runbooks - plus lineage documentation so your team understands every data flow.
Renting is local, so search had to understand a place and a date range as one question rather than two filters. Mirimera built the marketplace and the software our suppliers run on, and because it is one system underneath, nothing has ever had to be kept in sync.
- Pablo
CEO, Big RentalsData engineering stack
Modern tools for ingestion, transformation, and orchestration - chosen for reliability, scalability, and team familiarity.
Apache Kafka
Distributed event streaming for real-time data ingestion, pub/sub, and durable message queues at scale.
Apache Spark
Unified engine for large-scale batch and streaming data processing with SQL, ML, and graph capabilities.
- dbt
dbt
Transform raw warehouse data into analytics-ready models with version-controlled SQL and automated testing.
Snowflake
Cloud data warehouse with elastic scaling, separation of storage and compute, and native support for semi-structured data.
Airflow
Workflow orchestration for scheduling, monitoring, and retrying complex multi-step data pipelines.
- AWS
AWS Glue
Serverless ETL service for discovering, cataloging, and transforming data across your AWS data lake.
Ready to build reliable data pipelines?
Book a free 30-minute call. We'll review your data sources, SLAs, and downstream consumers - then outline a pipeline architecture.