Our talent solutions

Data engineers & software developers

Learn about our

Data engineers

What our data engineers can do

  • Designs and manages scalable, secure cloud infrastructure using AWS, Azure, and GCP, tailored to handle syndicated data, POS feeds, and commercial planning systems.
  • Implements modular data lake and lakehouse designs that enable clean ingestion, processing, and access to mission-critical datasets.
  • Balances cost, performance, and governance by applying CPG-specific tiering strategies and data lifecycle management.
  • Builds robust ETL/ELT pipelines from loyalty, eComm, retailer portal, ERP, and field sales data sources using dbt, Airflow, and Spark.
  • Standardizes ingestion across formats, cleanses inconsistencies, and joins disparate sources into a unified, business-ready layer.
  • Automates scheduling, exception handling, and logging to ensure high reliability and minimal downtime.
  • Implements real-time ingestion pipelines using Kafka and Spark Streaming for use cases like in-store stock alerts, pricing updates, and trade compliance tracking.
  • Enables dynamic dashboards, pricing triggers, and alerting systems for commercial and retailer account teams.
  • Ensures latency thresholds meet operational needs for same-day response and promotion execution.
  • Installs data quality frameworks that validate freshness, schema consistency, null handling, and rule violations.
  • Uses tools like Great Expectations and Monte Carlo to ensure business users trust their data and can trace its source.
  • Builds full data lineage visualizations and metadata repositories to support auditing and self-service.
  • Creates reusable, business-aligned datasets tailored to reporting and modelling use cases such as pricing performance, forecast history, and SKU velocity.
  • Collaborates closely with data scientists, analysts, and business users to align table design with real decisions.
  • Packages and deploys these curated data products with refresh automation, documentation, and field-tested naming conventions.

The infrastructure driving real business results

Unified 15+ data sources into a single ecosystem

For a regional grocery retailer, combined POS, loyalty, eCommerce, third-party, and ERP data into a governed lakehouse. Enabled end-to-end analytics and improved time-to-insight by 70%.

Built automated ETL with real-time data pipeline triggers using Kafka and Airflow. Supported merchandising, retail account distribution efficiency, and executive dashboards with live data.

 Designed an observability framework with schema drift checks, null detection, and late arrival flags. Reduced reporting errors and increased user confidence in data across sales and finance teams.

Created standardized data models for pricing, forecasting, and promotion analysis. Reduced custom SQL workload by 65% and enabled self-service analytics across 3 departments.

Why our data engineers deliver at scale

Deep understanding of CPG and retail data complexity

  • Understands how syndicated data, retailer POS, loyalty programs, and TPM systems connect within retail value chains.
  • Handles and aligns inconsistent or misaligned sources using tools designed for category managers and data scientists.
  • Aligns every pipeline to category cycles, promo timelines, and pricing hierarchies.

Accelerators that reduce setup time

  • Comes equipped with ingestion templates, data catalogs, entity matchers, and dimensional model frameworks.
  • Reduces upfront engineering load by reusing known logic patterns for retailer and brand ecosystems.
  • Enables MVP ingestion and model-ready views in weeks instead of months

Monitoring and quality control as a standard practice

  • QA is integrated into the pipeline, with each delivery including freshness checks, null filters, and field-level validation.
  • We embed alerts, test cases, and anomaly detection from day one.
  • This boosts trust, reduces post-launch issues, and improves model and dashboard accuracy.

Seamless integration with data science and software teams

  • Data engineers work hand-in-hand with modelers, product owners, and app developers from planning to deployment.
  • This avoids “handoff hell” and ensures upstream changes don’t break downstream business tools.
  • Every dataset is engineered with its downstream user in mind, whether human or algorithm.

Learn about our

Software developers

What our software developers can do

Custom front-end and UX development:
  • Designs highly intuitive, responsive interfaces using frameworks like React and Angular, tailored for category, trade, and field sales teams.
  • Builds visual workflows that include approval logic, inline decision support, and access control to support complex commercial use cases.
  • Aligns front-end experiences with brand design, user journeys, and platform constraints across desktop, tablet, and mobile.
  • Develops secure, scalable APIs and service architectures in Python, Node.js, or .NET that process AI outputs, enable real-time data sync, and connect to key systems (TPM, CRM, ERP).
  • Builds middleware layers that convert data science predictions or GenAI content into clear, usable endpoints.
  • Integrates robust testing, audit logs, caching, error handling, and monitoring in every service.
  • Builds low-code solutions for sales and marketing teams that embed business rules while reducing IT dependency.
  • Enables brand, commercial, and planning teams to create workflows, dashboards, and tools using governed templates.
  • Reduces development queue backlog and encourages local innovation through safe, modular configuration options.
  • Integrates pricing, forecasting, GenAI, and supply signals into dashboards, forms, and decision tools tailored to specific commercial moments.
  • Builds interfaces where business users can see recommendations, understand confidence levels, and act directly.
  • Turns analytics into direct actions by enabling next-step decisions in the same screen.
  • Implements CI/CD pipelines, containerization, and infrastructure-as-code to manage scalable deployments with rollback, testing, and monitoring.
  • Enables rapid iteration with full control over environments, versioning, and rollback procedures.
  • Supports SaaS, on-premises, and hybrid architectures and handles staging-to-prod transitions with full QA checkpoints.

Digital products that transform performance

Deployed trade planning app used by 200+ users weekly:

Developed a custom-built TPM app with front-end guardrails and back-end AI-assisted pricing logic. Helped optimize trade spend decisions and replaced 12 disconnected spreadsheets

Reduced report creation time by 90 percent with self-serve dashboards:

Built interactive BI tools that automatically pulled from cleaned datasets. Reduced dependency on analysts, allowing brand teams to build performance views in minutes, not days.

Launched merchandiser mobile app across 2,300 stores:

Built an offline-first app with GPS, image capture, and compliance scoring. Reduced the photo-to-action cycle from 48 hours to less than 4 hours and boosted representative productivity.

Developed SKU impact simulator for commercial teams:

Built a lightweight simulation tool with GenAI and data science integrations. Showed financial and distribution impact of pricing, promo, and assortment decisions in real time.

What sets our AI and GenAI experts apart

Built for non-technical commercial teams:
  • Interfaces are designed to match the real-world workflows of brand, trade, and shopper marketing teams, not developers.
  • Everything is simplified and focused around decision-making rather than configuration.
  • We test with real users to ensure adoption, speed, and clarity from day one.
  • Our developers don’t just write code—they handle full lifecycle delivery including QA, infrastructure setup, and support.
  • We reduce complexity by managing every piece including interface, integration, hosting, and user support, in one accountable team.
  • This approach makes releases faster and more reliable, with fewer handovers and less rework.
  • Every app is built to consume and activate outputs from data science models, forecasts, or GenAI engines.
  • UX elements display, explain, and contextualize predictive insights in a way that drives business decisions.
  • We bridge the gap between modeling teams and end users by turning models into practical, interactive tools.
  • Agile sprints, stakeholder demos, and real user testing are at the core of every build.
  • Releases are structured as co-created MVPs that evolve based on feedback from your commercial teams.
  • We deliver better tools by working with your people, not just for them.

Get in touch

Our friendly and efficient team is here to discuss your ideas. No pressure, just solutions.

Contact us

What happens next?

  • Our team will reach out to you to schedule a 'no pressure' call to help understand your objectives
  • We'll provide relevant demos and examples. You then confirm to us that you have a formal mandate to purchase.
  • We will provide the best-in-class proposition, tailored to all your nuances.
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