When data engineers build pipelines without quality frameworks, analysts inherit the quality problem. Cleaning and validating data early in the pipeline reduces repeated downstream effort.
Pipelines without monitoring and alerting fail silently until a dashboard shows zero or a report goes out with last week's numbers.
When the data engineer who built the pipeline leaves, their transformation logic leaves with them.
Rising Snowflake and BigQuery costs are often driven by inefficient query patterns, poor workload optimization, and suboptimal data modeling.
Align your technical infrastructure with business objectives through a comprehensive data roadmap. We help you move beyond reactive reporting to a proactive data culture by establishing the frameworks, standards, and architectures required for long-term scalability.
We design scalable storage architectures that transform fragmented data into a single source of truth. Our team builds high-performance environments tailored for complex querying, ensuring your business intelligence tools operate with maximum speed and accuracy.
Move data reliably across your ecosystem with automated, fault-tolerant pipelines. We specialize in engineering both real-time and batch-processing systems that ensure your data is always fresh, validated, and ready for consumption.
Protect the integrity of your data assets with automated quality checks and transparent lineage tracking. We implement rigorous governance frameworks that guarantee data reliability while maintaining strict compliance with global privacy standards.
Ensure the long-term reliability of your data ecosystem with proactive monitoring and continuous optimization. We provide the technical expertise needed to manage evolving schemas, maintain pipeline health, and scale your infrastructure as your data volume grows.
This phase includes source system inventory, current pipeline assessment, data quality evaluation, and query pattern analysis.
In this phase, the warehouse schema, the semantic layer, and the transformation framework are established. dbt project scaffolded, CI/CD for data configured, and data quality tests defined before any data flows through.
Priority pipelines built, tested, and validated against quality standards. Source system connectors integrated. Monitoring and alerting are configured. Runbooks written as pipelines are built.
Query performance optimization, cost review, and warehouse cost governance. Data team trained on dbt, pipeline management, and quality monitoring. Full documentation package delivered.
Migrated a national retailer from a legacy on-premise DWH to Snowflake. This project included 200+ source tables, a dbt transformation layer, and a semantic metrics layer serving 8 analytics tools.
Outcome:
Analytics query time reduced by 78%; data team pipeline maintenance time reduced by 60%; analyst trust in data significantly improved.
Built an event-driven data pipeline and warehouse for a B2B SaaS platform. We developed ingesting product usage events, billing data, and support tickets into a unified analytics layer for churn and expansion analysis.
Outcome:
Churn model training data available within 24 hours of event; product analytics latency reduced from T+2 days to real-time.
Implemented CDC-based pipeline and audit-ready data warehouse for a financial services firm. Our data engineers covered full data lineage, column-level security, and automated regulatory report generation.
Outcome:
Regulatory report preparation time reduced from 3 days to 4 hours; audit findings on data lineage eliminated.
Built HIPAA-compliant data integration layer connecting EHR, billing, scheduling, and patient engagement systems into a unified clinical analytics platform.
Outcome:
Clinical reporting turnaround reduced from weekly to daily; data quality issues discovered and resolved before impacting clinical decisions.
Our data pipeline development services go beyond traditional ETL by adopting modern ELT architectures. We build scalable, version-controlled pipelines with monitoring, alerting, and data quality checks to ensure reliability and faster data availability for analytics and machine learning.
Yes, we design pipelines for both batch and near real-time processing. Using modern tools and frameworks, our data engineering experts support event-driven architectures, streaming ingestion, and low-latency data delivery based on your business needs.
We work with technologies like Snowflake, Google BigQuery, Amazon Redshift, and Databricks, selecting the right solution based on your scale, performance requirements, and cost considerations.
Yes, every engagement of our data engineering services includes detailed documentation, runbooks, and team training to ensure your internal teams can manage data infrastructure independently.
We optimize costs by targeting the core drivers of unnecessary spend across compute, storage, and query usage. This includes redesigning data models to reduce heavy joins and repeated scans and optimizing queries and transformation logic to lower compute consumption. We also implement workload management practices such as query scheduling and resource isolation.
Since 1999, SunTec has been a trusted business process and software engineering partner for technology companies worldwide. Our team of over 1,500 full-time employees delivers innovative services every day to help our clients save time and money.
