• 15+ years of industry excellence

    Delivering innovative solutions to complex business challenges.

  • 50+ technical
    experts

    With specialized skills across diverse technology domains.

  • 200+ satisfied clients worldwide

    Spanning six continents and multiple industries.

  • 300+ successfully delivered projects

    Across retail, healthcare, finance, manufacturing, and other key sectors.
PROBLEMS WE HELP SOLVE
  • Hiring senior data engineers takes too long
    Our engineers can join your workflow within days and take over the work you don’t have time to wait months to hire for.
  • Analysts spend their time on ETL/dbt work instead of insights
    Plug in an engineer who can handle pipelines, dbt models, and refactors - and free your analysts to focus on real analysis.
  • ML ideas sit in backlog because there’s no one to prep the data or deploy models
    Add an engineer who can prepare datasets, build the needed pipelines, and turn ML ideas into working production workflows.
  • Cloud costs grow a bit faster than the game itself
    A senior engineer can review workloads, remove inefficiencies, and optimize processing so costs stay reasonable and predictable.
On-Demand Data Engineers
Embedded Into Your Team
  • Work directly in your tools and processes
    Engineers join your standups, workflows, and codebase as if they were part of your team.
  • Focus on the tasks you set
    They take on your priorities: pipelines, fixes, improvements, migrations, experiments.
  • Improve or set up the right tooling
    If needed, engineers propose and implement better practices, structures, and technologies.
  • Bring modern data engineering experience
    Practical experience with dbt, CI/CD, warehouse design, and cloud environments (AWS/GCP/Azure).
  • No hiring overhead, fast start
    No recruiting cycles. Engineers become productive within days.
  • Lean Spend
    Pay for delivered value, skip payroll drag

THE TALENT WE DELIVER

DATA ENGINEERS
Python • Scala • SQL • Airflow / Prefect / Dagster • Spark / Flink / Databricks • Kafka / Kinesis •
Snowflake • BigQuery • Redshift • ClickHouse • Delta Lake • Terraform • Docker • Kubernetes on AWS / GCP / Azure

AI ENGINEERS

Generative AI • LLM tuning • NLP • Computer Vision • PyTorch • TensorFlow • LangChain
DATA SCIENTISTS & ANALYSTS
Experimentation • Forecasting • Product & Growth analytics
BI DEVELOPERS
Tableau • Power BI • Looker • Qlik
DEVOPS & LLMOPS
CI/CD • MLflow • Kubeflow • ArgoCD • Helm • SageMaker
Choose the model that fits your roadmap
Flexible ways to add engineering capacity, depending on what your team needs
  • On-Demand Engineers
    Part-time or full-time
    Scale up or down at any moment. You only pay for the time you actually use
  • Dedicated Team
    2–3 Engineers + Team Lead
    Team Lead manages planning and delivery. Stable output and predictable timelines.
  • Hybrid
    Your engineers + our Team Lead + extra capacity when required.
Book a Call
A few examples of where we could help
  • Building or stabilizing data pipelines
    If your ETL/ELT jobs refresh too slowly, break during releases, or aren’t predictable, our engineers can take full ownership and bring them to a stable state.
  • Cleaning up and structuring data models
    If your warehouse feels messy or inconsistent - tables, models, or definitions - we can reorganize and align everything so your analysts and product teams work with clean, reliable data.
  • Unifying event data across titles
    If different projects use different event formats or naming, we can standardize and validate event data to give your studio one clear, trusted source of truth.
  • Making dashboards and reporting reliable
    If dashboards refresh manually, break unexpectedly, or show conflicting numbers, we can automate and stabilize reporting so it stays consistent and dependable.
  • Getting ML workflows into real use
    If churn, retention/LTV, anomaly detection or other ML ideas get stuck at the “prototype” stage, we can prepare the data and build the pipelines needed to run them in practice.
  • Keeping cloud usage under control
    If cloud spend grows faster than expected, we can review workloads, simplify processing, and streamline pipelines so usage stays reasonable and predictable.
Technologies
  • AI Models & Agents
    OpenAI, Claude, Llama, Mistral (OCR), Elevenlabs, Bark, Whisper.
  • Frameworks & Agent Tech
    RAG, LangChain, LlamaIndex, A2A, MCP.
  • Cloud AI
    AWS Bedrock, Google Gemini, Vertex AI.
  • ML & Advanced Analytics
    TensorFlow, PyTorch, scikit-learn, OpenCV, pandas, NumPy, Matplotlib, Seaborn.
  • Dev & Engineering
    React, Angular, TypeScript, Java/Kotlin, Node.js, Python (Flask, Django), React Native.
  • Cloud & DevOps Tools
    AWS, Azure, GCP, Docker, Jenkins.
  • Databases
    PostgreSQL, Snowflake, Databricks, MS SQL, Redshift, Oracle, BigQuery, MongoDB, MySQL, DynamoDB.
  • ETL/Processing
    SSIS, SSAS, dbt, Airflow, Glue, Fivetran, Airbyte, DataStage, Cloud Composer.
  • Visualization
    Power BI, Tableau, Qlik, Looker.
  • Cloud-specific services
    AWS (S3, RDS, MWAA), GCP (Cloud Storage, Data Fusion), Azure (Data Factory, SQL Database).

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