Reliable Data Pipelines That Your Business Can Trust
We design and build data pipelines that ingest from any source, transform with documented logic, and deliver clean, governed datasets to your warehouse and consumers, on time, every time.
Data Engineering
- dbt Transformations
- Ingestion Pipelines
- Airflow Orchestration
- Streaming Pipelines
ETL/ELT Pipeline Engineering
Modern data engineering favors ELT — load raw data into your warehouse first, transform in-place using SQL with dbt. We build both patterns based on your requirements.
Get Started- ELT pipelines using dbt for warehouse-native transformations
- ETL pipelines with PySpark for complex transformation logic
- Incremental loading strategies for high-volume sources
- Schema evolution handling and backward compatibility
- Data lineage and documentation via dbt docs
Data Ingestion & Connectors
Getting data in reliably is half the battle. We build and configure ingestion from databases, SaaS tools, APIs, files, and streaming sources.
Get Started- Fivetran and Airbyte for managed connector ingestion
- Custom Python connectors for non-standard sources
- CDC (change data capture) for real-time database replication
- File ingestion from S3, SFTP, and cloud storage
- Webhook and API ingestion with retry and deduplication
Pipeline Orchestration & Scheduling
Orchestration ensures pipelines run in the right order, handle failures gracefully, and alert on issues before downstream consumers are affected.
Get Started- Apache Airflow and Astronomer for DAG orchestration
- Dagster for data-asset-centric pipeline management
- Prefect for Python-native workflow orchestration
- Dependency management and cross-pipeline scheduling
- SLA alerting and on-call runbooks for data teams
Data Quality & Testing
Bad data is worse than no data. We implement quality checks at every stage of the pipeline to catch issues before they reach dashboards and ML models.
Get Started- dbt tests: not-null, unique, referential integrity
- Great Expectations for custom data quality assertions
- Schema drift detection and alerting
- Data freshness monitoring and SLA alerts
- Quarantine patterns for failed quality records
What We Deliver
A comprehensive set of Data Engineering capabilities, designed to work together or independently.
dbt Transformations
SQL-based transformation layers with testing, documentation, and lineage tracking.
Ingestion Pipelines
Fivetran, Airbyte, and custom connectors for all data sources.
Airflow Orchestration
DAG-based orchestration ensuring pipelines run reliably in the right order.
Streaming Pipelines
Kafka and Flink pipelines for real-time data ingestion and processing.
Data Quality Framework
Automated quality tests catching schema drift, nulls, and anomalies before dashboards.
Pipeline Monitoring
SLA alerting, freshness monitoring, and on-call runbooks for production pipelines.
Production pipelines operated with SLA-backed uptime and on-call support.
Most pipelines deliver data freshness within 1 hour of source events.
Every dataset documented with full column-level lineage from source to consumption.
Why Choose InnovTen
We don't just deliver projects. We build partnerships that drive long-term outcomes.
Quality at the Source
Data quality checks run before bad data reaches dashboards or ML models.
Self-Documenting Pipelines
dbt's documentation layer means every dataset has up-to-date column descriptions and lineage.
Reliable Freshness
SLA monitoring and alerting ensure stakeholders always have fresh, reliable data.
Cost-Efficient Processing
Incremental loading and ELT patterns minimize compute costs for large-volume pipelines.
Maintainable by Design
Modular pipeline architecture and code reviews ensure your data team can own the codebase.
Data Team Enablement
We train and embed best practices so your data engineers can extend what we build.
Our Delivery Process
How we approach every Data Engineering engagement, from first call to ongoing operations.
Source Discovery
Inventory all data sources, access methods, data volumes, and freshness requirements.
Architecture Design
Design ingestion layer, transformation strategy, and orchestration topology.
Pipeline Development
Build ingestion connectors, dbt models, and orchestration DAGs with tests.
Quality & Monitoring
Implement quality assertions, freshness monitors, and alerting across all pipelines.
Handover & Documentation
dbt docs site, runbooks, and knowledge transfer to your data engineering team.
Source Discovery
Inventory all data sources, access methods, data volumes, and freshness requirements.
Architecture Design
Design ingestion layer, transformation strategy, and orchestration topology.
Pipeline Development
Build ingestion connectors, dbt models, and orchestration DAGs with tests.
Quality & Monitoring
Implement quality assertions, freshness monitors, and alerting across all pipelines.
Handover & Documentation
dbt docs site, runbooks, and knowledge transfer to your data engineering team.
Data Engineering in Action
Real-world applications across industries we've delivered for.
Multi-Source Data Warehouse
Unified pipeline ingesting from Shopify, Salesforce, and NetSuite into Snowflake, delivering fresh data every 30 minutes.
CDC Replication Pipeline
Change data capture from transactional PostgreSQL to BigQuery for analytics, with sub-5-minute lag at 50M events/day.
Data Platform Migration
Migrated legacy SSIS pipelines to dbt and Airflow, cutting pipeline runtime from 8 hours to 45 minutes.
Streaming Ingestion
Kafka pipeline ingesting 10M sensor events/hour into Databricks Delta Lake for real-time equipment monitoring.
Frequently Asked Questions
Common questions about our Data Engineering services.
dbt is the right choice for warehouse-native SQL transformations: it's simpler, faster to develop, and the documentation and testing features are excellent. Spark is better for large-scale data processing where you need distributed compute outside the warehouse, complex Python logic, or ML feature engineering.
Fivetran is fully managed, requires no maintenance, and has the broadest connector library, ideal if you want to move fast and the cost is acceptable. Airbyte is open-source, self-hosted (or cloud), more customizable, and significantly cheaper. We help you choose based on your connector needs and budget.
We implement schema drift detection that alerts your team when a source changes. For critical pipelines, we add automated schema evolution handling that propagates compatible changes downstream and quarantines incompatible ones for review.
A single source-to-warehouse pipeline typically takes 1–2 weeks including ingestion, transformation, testing, and monitoring. A full data platform with 10+ sources, semantic layer, and documentation takes 2–3 months.
Ready to Get Started with Data Engineering?
Tell us about your project. We'll respond within 24 hours with a clear next step.