Enterprise-Grade Data Engineering

Sync. Validate.
Scale Without Limits.

We build industry-agnostic cloud ETL pipelines, automated QA frameworks, and data validation systems that turn raw data chaos into reliable, actionable intelligence.

▸ Live ETL Pipeline
Data Sources
→
Ingest
Transform
→
Validate
Cleanse
→
Load
3 active pipelines All Systems Nominal
40+
ETL Pipelines Deployed
99.8%
Pipeline Uptime SLA
12B+
Records Processed
6
Industry Verticals Served
Why MobileAgent

Built for Scale.
Designed for Reliability.

From ingestion to validation, every component of our stack is engineered to run at enterprise velocity with zero compromise on data integrity.

Cloud-Native ETL Pipelines

Serverless, auto-scaling pipelines on AWS, GCP, and Azure. No infrastructure overhead — just clean, flowing data.

Automated QA Frameworks

Selenium and Pytest-powered test suites that catch data anomalies before they reach production — automatically.

Industry-Agnostic Design

From restaurant POS systems to logistics networks — our connectors and schemas adapt to any vertical out of the box.

Real-Time Stream Processing

Apache Kafka and Spark Streaming integrations for sub-second latency on mission-critical data flows.

End-to-End Data Security

AES-256 encryption at rest and in transit. SOC 2-ready audit trails and role-based access on every pipeline.

Observability & Monitoring

Real-time dashboards, anomaly alerts, and lineage graphs so your team always knows exactly what the data is doing.

Our Process

From Discovery to Deployment

A lean, repeatable 4-step engagement model that gets your pipeline live in weeks, not months.

01

Discovery & Audit

Map existing data sources, schemas, and pain-points across all systems.

02

Pipeline Design

Architect the ETL flow, validation rules, and cloud infrastructure blueprint.

03

Build & Test

Develop pipelines with automated Pytest suites running on every commit.

04

Deploy & Monitor

Go live on cloud with full observability, alerting, and SLA dashboards.

Ready to Sync Your Data Stack?

Let's scope your pipeline in a free 30-minute technical discovery call.

What We Do

Cloud ETL & Data Services

Industry-agnostic data engineering services — built on modern cloud infrastructure, designed to handle any volume, velocity, or variety of data.

How Data Flows

Our End-to-End ETL Architecture

Every pipeline we build follows a proven 7-stage flow — from raw source ingestion to validated, query-ready output.

1

Source

APIs, DBs, Files, Streams

→
2

Ingest

Kafka, Firehose, Pub/Sub

→
3

Stage

Raw Landing Zone

→
4

Transform

dbt, Spark, Pandas

→
5

Validate

Pytest, Great Expectations

→
6

Load

Warehouse, Data Lake

→
7

Observe

Dashboards, Alerts, Logs

Supported Cloud Platforms & Tools
AWS
Google Cloud
Azure
⚡ Apache Kafka
🔥 Apache Spark
🧱 dbt
❄️ Snowflake
🧊 Databricks
🐍 Python / Pandas
🔬 Great Expectations
Core Services

Everything Your Data Stack Needs

Six specialised service lines — each delivered as a standalone engagement or as part of a full-stack data transformation programme.

Cloud ETL Pipeline Engineering

Design, build, and maintain fully managed ETL/ELT pipelines on AWS Glue, GCP Dataflow, or Azure Data Factory.

  • Batch & real-time ingestion architectures
  • Schema evolution & backward compatibility
  • Auto-scaling on serverless compute
  • CI/CD pipeline deployments via Terraform
  • SLA-backed uptime monitoring (99.8%+)
AWS Glue Dataflow Terraform Airflow

Data Validation & Quality Gates

Automated data quality enforcement at every pipeline stage — no bad data reaches your warehouse or downstream consumers.

  • Schema, type, and constraint validation
  • Null rate & outlier threshold checks
  • Referential integrity across joined datasets
  • Great Expectations test suite generation
  • Slack / PagerDuty alerting on failures
Great Expectations Pytest dbt tests PagerDuty

Real-Time Stream Processing

Sub-second data pipelines for event-driven architectures — powering live dashboards, fraud detection, and IoT systems.

  • Apache Kafka topic design & partitioning
  • Spark Streaming & Flink job development
  • Event schema registry management
  • Exactly-once delivery guarantees
  • Dead-letter queue handling & replay
Kafka Spark Streaming Flink Kinesis

Data Warehouse & Lakehouse Design

Medallion architecture implementation on Snowflake, BigQuery, and Databricks — structured for analytics velocity.

  • Bronze / Silver / Gold layer modelling
  • dbt model development & documentation
  • Slowly Changing Dimension (SCD) handling
  • Query optimisation & cost governance
  • Role-based access control (RBAC) setup
Snowflake BigQuery Databricks dbt

Third-Party API & System Integration

Connect any SaaS platform, legacy system, or external data provider into your unified data ecosystem seamlessly.

  • REST & GraphQL API connectors
  • OAuth 2.0 & API key authentication handling
  • Rate-limit management & retry logic
  • Webhook ingestion & event normalization
  • Legacy database CDC (Change Data Capture)
REST APIs GraphQL Fivetran Airbyte Debezium

Pipeline Observability & Monitoring

Full-spectrum visibility into your data pipelines — latency, row counts, drift detection, and business KPIs in one place.

  • Data lineage graph generation & tracking
  • Row count & volume anomaly detection
  • Pipeline latency SLA dashboards
  • Grafana & DataDog integration
  • Custom alerting rules per dataset owner
Grafana DataDog Monte Carlo OpenLineage
Industries Served

One Platform. Every Vertical.

Our pipeline templates and connectors are pre-built for six major industry verticals — go live faster with less custom work.

Restaurant & Hospitality

POS sync, inventory feeds, reservation data pipelines

Transport & Logistics

Fleet tracking, route data migration, shipment telemetry

Supply Chain & Agriculture

Tea, commodity & raw material traceability ingestion

Mobile & SaaS Platforms

App event ingestion, user behaviour analytics, A/B data

Retail & E-Commerce

Order, inventory, returns & pricing data warehouse feeds

Healthcare & Life Sciences

FHIR data pipelines, clinical trial ingestion, HIPAA-ready

Service Comparison

Which Service Fits Your Needs?

A quick breakdown of our core offerings across key delivery dimensions.

Capability ETL Pipeline Stream Processing Data Warehouse Observability
Real-Time Data — ✓ — ✓
Batch Processing ✓ — ✓ ✓
Data Validation ✓ ✓ ✓ ✓
Cloud Agnostic ✓ ✓ ✓ ✓
Schema Management ✓ ✓ ✓ —
Alerting & Notifications — ✓ — ✓
CI/CD Integration ✓ ✓ ✓ —

Not Sure Which Service You Need?

Book a free technical scoping call — we'll map the right solution to your stack.

Case Studies

Real Projects. Real Results.

Five detailed case studies across SaaS, hospitality, supply chain, logistics, and Microsoft Fabric data warehousing — each solving a unique data engineering challenge at scale.

5
Completed Projects
3x
Avg. Performance Gain
99.9%
Avg. Pipeline Uptime
60%
Avg. Cost Reduction
All Projects

Case Study Deep Dives

Each engagement is documented end-to-end — from the initial problem statement to measurable production outcomes.

SaaS · QA Automation

Mobile Agent SaaS Platform

Selenium Pytest CI/CD
The Challenge

A mobile SaaS startup had zero automated test coverage across their agent dashboard and API layer. Every release required 3 days of manual regression — blocking weekly deployments.

What We Built
  • End-to-end Selenium test suite covering 120+ UI flows
  • Pytest API layer with 300+ parameterised test cases
  • GitHub Actions CI pipeline — tests run on every PR
  • Allure HTML reporting with screenshot capture on failure
  • Page Object Model (POM) architecture for maintainability
Outcomes
94%
Test Coverage Achieved
3hr
Regression Cut to Minutes
0
Production Regressions Post-Launch
Tech Stack
Selenium 4 Pytest Python 3.11 GitHub Actions Allure Docker Page Object Model
Hospitality · ETL · Cloud Sync

Restaurant POS Data Sync

Real-Time Sync AWS PostgreSQL
The Challenge

A multi-branch restaurant chain had 14 isolated POS systems with no central data view. Nightly manual exports caused 12-hour reporting delays and frequent reconciliation errors.

What We Built
  • Real-time CDC pipeline from 14 POS instances via Debezium
  • AWS Kinesis stream with Lambda transformation layer
  • Centralised PostgreSQL data warehouse on RDS
  • dbt models for sales, inventory, and labour reporting
  • Automated schema validation on every sync cycle
Outcomes
12hr
Lag Reduced to <30 Seconds
14
POS Branches Unified
100%
Reconciliation Accuracy
Tech Stack
AWS Kinesis Debezium Lambda PostgreSQL dbt Terraform Grafana
Supply Chain · Data Ingestion

Tea Supply Chain Data Ingestion

GCP BigQuery Batch ETL
The Challenge

A regional tea distributor managed procurement, quality grades, and shipment records across 6 spreadsheet systems and 3 legacy databases with no traceability or unified reporting layer.

What We Built
  • Unified ingestion pipeline from Excel, CSV, and MySQL sources
  • GCP Cloud Storage landing zone with Dataflow batch jobs
  • BigQuery data warehouse with commodity traceability schema
  • Quality grade normalisation and supplier deduplication logic
  • Looker Studio dashboard for procurement and audit teams
Outcomes
6
Systems Consolidated into One
80%
Reporting Time Saved Weekly
100%
Batch Traceability Coverage
Tech Stack
GCP Dataflow BigQuery Cloud Storage Python Pandas Looker Studio Cloud Scheduler
Logistics · Cloud Migration

Transport Logistics Data Migration

Azure Snowflake Migration
The Challenge

A national logistics firm needed to migrate 8 years of on-premise fleet, route, and delivery data to Azure cloud with zero downtime and full historical fidelity preserved.

What We Built
  • Azure Data Factory migration pipeline for 2.4TB of legacy data
  • Snowflake target warehouse with partitioned route history tables
  • Automated Pytest validation suite — row count and checksum checks
  • Dual-write cutover strategy ensuring zero data loss during switch
  • Post-migration data reconciliation report with 100% sign-off
Outcomes
2.4TB
Data Migrated Flawlessly
0
Minutes of Downtime
65%
Query Performance Improvement
Tech Stack
Azure Data Factory Snowflake Azure Blob Storage Pytest Python SQL Server Power BI
Data Warehousing · Microsoft Fabric · Testing

Microsoft Fabric Warehouse Validation

Microsoft Fabric Data Warehouse Testing
The Challenge

A finance data warehousing team needed to validate ingestion, transformation, and reporting layers in Microsoft Fabric while preventing broken loads, duplicate rows, and schema drift from reaching analysts.

What We Built
  • Microsoft Fabric Lakehouse and Warehouse setup for curated finance reporting
  • Automated testing for schema checks, row counts, and primary key uniqueness
  • Data quality assertions for null handling, referential integrity, and duplicate detection
  • Validation pipeline aligned to notebook refreshes and warehouse load cycles
  • Test evidence captured for release sign-off and audit readiness
Outcomes
98%
Validation Coverage Across Critical Tables
0
Defective Loads Reaching Reports
75%
Faster Release Verification
Tech Stack
Microsoft Fabric Fabric Warehouse Lakehouse SQL Pytest Data Quality Checks Power BI

Have a Similar Challenge?

Tell us about your data problem — we'll propose a solution within 48 hours.

Quality Assurance

Testing That Catches Everything.

Two disciplined QA tracks — automated framework engineering with Selenium & Pytest, and structured manual verification protocols — working together for complete coverage.

Automated Testing

Selenium & Pytest Frameworks

Production-grade automated test suites built for speed, maintainability, and deep coverage — across UI, API, and data pipeline layers.

Selenium UI Test Suites

Browser-level end-to-end tests that simulate real user journeys across web and mobile dashboards.

  • Page Object Model (POM) architecture
  • Cross-browser testing — Chrome, Firefox, Edge
  • Headless execution in CI/CD pipelines
  • Screenshot & video capture on failure
  • Dynamic element wait strategies (explicit/fluent)
# Example: POM-based login test def test_agent_login(driver): page = LoginPage(driver) page.enter_credentials( "agent@saas.io", "secure123" ) page.click_login() assert page.dashboard_visible()
Selenium 4 WebDriver ChromeDriver POM Pattern

Pytest API Test Frameworks

Parameterised, fixture-driven API test suites validating REST endpoints for correctness, performance, and security.

  • Parameterised test cases with data-driven inputs
  • Request/response schema validation via Pydantic
  • Auth token lifecycle & session management testing
  • Load threshold & response time assertions
  • Allure HTML reporting with full request logs
# Parameterised API endpoint test @pytest.mark.parametrize("endpoint,status", [ ("/api/agents", 200), ("/api/sync", 200), ("/api/admin", 403), ]) def test_endpoints(client, endpoint, status): res = client.get(endpoint) assert res.status_code == status
Pytest Requests Pydantic Allure

Pipeline Data Validation Tests

Automated Pytest suites that validate data integrity at every stage of the ETL pipeline — pre and post load.

  • Row count reconciliation between source and target
  • Checksum & hash-based record integrity checks
  • Null rate, type conformance & range assertions
  • Duplicate detection and deduplication verification
  • Great Expectations integration for expectation suites
# Pipeline row count validation def test_row_count_match(source_db, target_db): src = source_db.count("orders") tgt = target_db.count("stg_orders") assert src == tgt, f"Mismatch: {src} vs {tgt}"
Great Expectations Pytest SQLAlchemy Pandas

CI/CD Pipeline Integration

Tests wired directly into deployment workflows — every commit is validated before it ever reaches staging or production.

  • GitHub Actions & GitLab CI workflow configuration
  • Docker-containerised test environments per run
  • Parallel test execution across matrix configurations
  • Automatic PR status gates — merge blocked on failure
  • Slack notifications with pass/fail summary per build
# GitHub Actions test job jobs: test: runs-on: ubuntu-latest steps: - uses: actions/checkout@v3 - run: pip install -r requirements.txt - run: pytest --alluredir=./reports
GitHub Actions Docker GitLab CI Pytest-xdist
Test Coverage Benchmarks

Typical Coverage Achieved Across Project Types

UI / End-to-End (Selenium) 92%
API Endpoints (Pytest) 97%
Data Pipeline Validation 95%
Schema & Type Conformance 100%
Manual Verification

Manual Type & Data Verification

Structured, protocol-driven manual QA processes that catch what automation cannot — edge cases, visual nuances, business logic ambiguity, and data semantic correctness.

Data Type Verification

Manual inspection of data types across every field in source, staging, and target layers to catch silent type coercion errors automation may miss.

  • Integer vs. string boundary checks on numeric IDs
  • Date format consistency across time zones
  • Decimal precision validation on financial fields
  • Boolean representation (0/1, true/false, Y/N) normalisation
  • Null vs. empty string distinction verification
Type Mapping Docs SQL Profiling Excel Audits

Schema & Structure Audits

Manual review of table schemas, column naming conventions, and data dictionary alignment between source systems and targets.

  • Column-by-column source-to-target mapping review
  • Naming convention compliance (snake_case, prefixes)
  • Primary key and foreign key integrity walkthroughs
  • Index and partition strategy review for query performance
  • Data dictionary sign-off with stakeholders
Data Dictionary ERD Review SQL Profiler

UAT & Business Logic Verification

Stakeholder-facing user acceptance testing that validates business rules, calculated fields, and reporting figures against source-of-truth documents.

  • Business rule walkthroughs with domain owners
  • Calculated metric spot-checks against manual totals
  • Report figure reconciliation against legacy system
  • Edge case scenario testing with real data samples
  • Sign-off checklists per data domain and stakeholder
UAT Scripts Confluence Jira

Performance & Volume Spot Testing

Manual benchmarking of query performance and pipeline throughput under realistic data volumes before full production load.

  • Query execution plan reviews in Snowflake / BigQuery
  • Manual volume test runs with production-scale datasets
  • Timeout threshold & memory usage observations
  • Bottleneck identification in transformation logic
  • Before/after performance comparison documentation
Query Profiler EXPLAIN ANALYZE Snowflake UI

Sample-Based Record Verification

Statistically sampled row-level inspection comparing source records directly against loaded target records for field-level accuracy.

  • Random stratified sampling across data partitions
  • Field-by-field comparison on 200+ sampled records
  • Transformation logic trace from source to target
  • Anomalous record flagging and root cause analysis
  • Written verification report per data domain
SQL Queries Excel Comparison Sampling Scripts

Security & Access Control Checks

Manual verification that data access policies, row-level security rules, and PII masking are functioning correctly across all user roles.

  • Role-based access testing per user persona
  • PII field masking verification in non-prod environments
  • Column-level security policy walkthroughs
  • Audit log review for unauthorised access attempts
  • GDPR & data residency compliance spot checks
RBAC Review Audit Logs GDPR Checklist
Approach Comparison

Automated vs. Manual — When to Use Each

Both tracks are complementary — not competing. Here's how we decide which to apply at each project stage.

Test Scenario Automated Manual Recommended
Regression Testing ✓ — Automated
Row Count Reconciliation ✓ ✓ Automated
Business Logic UAT — ✓ Manual
Data Type Verification ✓ ✓ Both
Edge Case Exploration — ✓ Manual
CI/CD Gate Enforcement ✓ — Automated
Security & Access Review — ✓ Manual

Want Full QA Coverage on Your Project?

We'll audit your current testing gaps and propose a tailored automation strategy.

Get In Touch

Let's Build Something Reliable.

Tell us about your data challenge — we'll respond within 24 hours with a technical proposal.

Send a Message

Start the Conversation

Contact Details
Email
hr@mobileagentllc.co
Website
www.mobileagentllc.co
Response Time
Within 24 hours
What Happens Next
  • We review your submission and technical requirements
  • A senior engineer schedules a 30-min discovery call
  • We deliver a scoping document within 48 hours
  • Project proposal with timeline and cost estimate
Currently Accepting New Projects
Next available sprint starts July 2026