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Data & Analytics Engineer

Summary

Build and maintain data pipelines, dashboards, and automation tools using SQL and Azure data services, ensuring data reliability for client dashboards and products.

Build data pipelines, dashboards, and automation tools.

As a Data & Analytics Engineer at ITCommence, you’ll build and maintain the data layer everything else depends on: ingestion pipelines, transformation logic, and warehouse models that feed client dashboards, predictive models, and our own products, e‑Sign and Graphique. The stack centers on SQL and Azure data services, with real ownership over reliability: when a client’s morning numbers are wrong, you’re the person who can say why. You’ll work with our engineering and BI teams across the US and our Chennai delivery center.

Responsibilities

  • Design and build data pipelines that ingest from ERPs, APIs, files, and operational databases.
  • Develop and maintain warehouse and lakehouse models optimized for reporting and analytics.
  • Implement data quality checks, monitoring, and alerting so problems surface before users see them.
  • Support BI consultants and data scientists with clean, documented, refresh‑reliable datasets.

What You’ll Do

  • Write and optimize SQL transformations and manage scheduled refresh processes.
  • Build integrations with Azure Data Factory, Synapse pipelines, or comparable tooling.
  • Reconcile conflicting definitions across source systems into agreed, documented entities.
  • Participate in architecture decisions for new client engagements.

Requirements

Required Skills

  • 3+ years in data engineering or analytics engineering roles.
  • Strong SQL and experience with a modern warehouse (Synapse, Snowflake, BigQuery, or similar).
  • Hands‑on pipeline development with ADF, dbt, SSIS, or comparable tools.
  • Python for data processing and automation.
  • A track record of owning data reliability, not just building and moving on.

Nice to Have

  • Azure data certifications (DP-203 or equivalent experience).
  • Experience with streaming or near‑real‑time ingestion.
  • Familiarity with Power BI datasets and how modeling choices affect them.
  • Exposure to CI/CD for data workflows.

See also

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