90% of AI and ML initiatives fail or stall because of unreliable data pipelines. We build the pipelines, warehouses, and governance layer that keep yours from being one of them.
CMMI
Level 3
HIPAA
Compliant
AWS
Partner Network
ISO Certified
For Data Quality & Security
SOC 2
Type II Compliant
Growing data volume exposes gaps that ad hoc scripts and part-time data ownership cannot fix. Our expert-led data engineering services resolve these issues before they become bigger business problems.
Data scattered across disconnected CRMs, ERPs, and ad hoc spreadsheets creates conflicting metrics and time-consuming manual reconciliations. This is the same as different departments having different numbers for the same metric.
AI agents that plan and execute approved multi-step workflows using defined tools, permission boundaries, and human escalation paths.
AI and ML models depend on structured, clean, and well-governed data pipelines. Without proper data engineering, they produce hallucinated, biased, or unusable outputs. For your AI investment, this means the data feeding it is the problem, and no amount of prompt engineering fixes that.
Batch-only data infrastructure cannot feed live context to AI, fraud detection, or real-time recommendation engines that require up-to-the-second data streams. For real-time ops, this means your real-time use case is running on yesterday’s data.
And this is the backdrop against which our data engineers operate.
From initial data strategy to ongoing support, our data engineering services address every specific stage of your data journey.
We assess your existing data infrastructure, sources, and business goals to define a target architecture and phased roadmap, drawing on frameworks like the AWS Well-Architected Framework or Azure Cloud Adoption Framework. You get a documented data engineering roadmap with cost and timeline estimates you can take to your board; not a slide deck of generic best practices.
We architect and build batch and streaming pipelines using tools like Apache Airflow, Kafka, and Spark, with monitoring, alerting, and retry logic from the start. Our data engineers build every pipeline rightly sized to your actual data volume and configured for your latency needs, giving your team a scalable infrastructure they can rely on.
We migrate on-premises or legacy cloud data infrastructure to AWS, Azure, or GCP using Terraform-driven, phased rollouts that keep existing systems running throughout the transition. Our cloud migration experts plan your account migration upfront based on data volume, downstream dependencies, and cutover risk. Your teams keep working through the migration; there’s no “big bang” cutover risk to explain to your board.
Design and implement modern warehouses and lakehouses on Snowflake, BigQuery, Redshift, or Databricks, matched to your scale and query patterns. Our data engineers run legacy warehouse migrations with minimal downtime and validated data parity, giving teams a single source of truth for analytics instead of scattered spreadsheets and marts.
Build and modernize extraction, transformation, and load workflows, including migrating hand-rolled scripts into version-controlled dbt models. We orchestrate ETL/ELT pipelines through tools like Airflow or Dagster for full visibility into every run, closing the key-person dependency risk that legacy, undocumented ETL setups may create for your data teams.
We build event-driven data infrastructure using Kafka, Flink, or Kinesis for use cases that cannot wait on a nightly batch job, such as fraud scoring or live operational dashboards. Our data engineering experts optimize for throughput and low latency, with observability built in from day one to support faster decision-making.
Unify disparate SaaS platforms, ERPs, and CRMs into a consolidated data model using REST/GraphQL APIs, webhook receivers, and CDC (change data capture) tools like Debezium. We design for complex M&A integrations and cross-system syncs to eliminate manual CSV exports and maintain reliable, bidirectional data syncs.
Architect distributed processing systems using Spark, Hadoop, or cloud-native equivalents for datasets too large or complex for conventional tooling. Our data engineering service tunes all data jobs for cost and performance, covering everything from log processing to large-scale feature engineering. Your infrastructure scales with data growth instead of stalling.
We implement validation rules, lineage tracking, and access controls using frameworks like Great Expectations and catalog tools such as DataHub or Collibra to catch flawed data before it reaches a dashboard. Our data engineers build governance guardrails around your compliance requirements, such as SOC 2, HIPAA, or GDPR, without slowing delivery.
If needed, we can assign dedicated data engineers to monitor and maintain production pipelines, warehouses, and integrations post-launch. We use observability tools like Datadog, Grafana, or Prometheus to catch failures before they affect business reporting. Our support also covers performance tuning, dependency upgrades, and incident response, keeping infrastructure reliable well beyond the initial deployment.
Refactor your technical debt into version-controlled, fully
orchestrated dbt models.
We bridge the gap between raw data and actionable intelligence by building resilient, high-performance data architectures that your teams can take over. Our approach ensures your data is accessible, governed, scalable, and engineered to meet the demands of modern analytics and AI.
| Feature | Off-the-Shelf Tools | In-House Build | Our Data Engineering Service |
|---|---|---|---|
| Documentation Quality | Minimal | Internal only | Data dictionaries + runbooks delivered |
| Data Quality Framework | Optional add-on | Bolted on later | Built into every data engineering pipeline |
| dbt Adoption | Sometimes | Inconsistent | Default, version-controlled from day one |
| Observability | Not included | Manual logging | Monitoring + alerting on every pipeline |
| Semantic Layer | Rarely included | Often skipped | Defined before the warehouse build |
| Team enablement | Not included | Internal knowledge | Full training + documentation |
A green job status doesn’t mean your data is clean. Stop letting broken payload
mutations quietly corrupt downstream models and executive reports.
Our data engineers identify target sources, schema constraints, and SLA goals to build batch and real-time streaming data.
We cleanse raw payload feeds, enforce schema validation, and execute business logic.
Our experts implement datasets across cloud lakehouses and warehouses, and RBAC and lineage tracking to ensure governance.
We unify semantic layers, custom APIs, and reverse ETL tools to serve business-ready metrics directly to end users.
See our case studies to discover our data engineering expertise in modernizing legacy systems, optimizing cloud architectures, and delivering resilient, high-throughput data infrastructure.
Solved an acute problem
A commercial lender's underwriters spent 20–30 hours per loan file on manual data entry. We built a custom-trained document processing and workflow platform on AWS.
35%
higher throughput
99.5%
extraction accuracy
Rebuilt a broken core system
A bath & lighting brand in 55+ countries had site search converting just 10–15% of searchers. We trained a transformer ranking model on their own catalog and query logs.
80%
more accurate search
72%
higher conversion
Found the bigger problem
An eCommerce retailer brought us in for nopCommerce upkeep. The real bottleneck was inventory and pricing scattered across five channels — so we built the fix.
60%
increase in sales
~90%
tasks automated
Provided a dedicated engineering pod
A core retail POS platform had to outgrow three technology eras without losing what made it work. The same embedded pod carried it from CGI to PHP to AWS over 15 years, so no knowledge was ever lost in handover.
15+ years
Zero re-tendering and handovers
The timeline for a data engineering project depends on scope. Initial data audits and pipeline refactoring take a few weeks, while full enterprise lakehouse implementations or cloud migrations take 8 to 16 weeks, and sometimes longer. We offer flexible engagement models, including dedicated engineering teams, project-based delivery, and ongoing DataOps managed services.
Technoscore’s data engineering experts specialize in modern cloud platforms including Snowflake, Databricks, Google BigQuery, AWS Redshift, and Azure Synapse. For orchestration and transformation, we work extensively with dbt, Apache Airflow, Dagster, Apache Kafka, and PySpark.
Most data engineering engagements build on existing infrastructure rather than replacing it outright. We first assess the pipelines, warehouses, and integrations already in place, so we rebuild or migrate only the components genuinely limiting performance, reliability, or scale.
Our data engineers implement strict Role-Based Access Controls (RBAC), column-level data masking, end-to-end encryption, and automated lineage tracking. This ensures your data infrastructure complies with regulatory frameworks such as SOC 2, HIPAA, GDPR, and CCPA.
You retain full ownership of all data, pipeline code, infrastructure configurations, and documentation produced during the engagement. Technoscore’s data engineers deliver everything in a form your internal team can access, modify, and run independently without ongoing dependency on external support.
Project initiation within 48 hours
Round-the-clock support
Strict NDAs for data security
Share your project requirements.
Data dictionaries + runbooks delivered
