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Anicca Data Science Solutions Linkedin · Posted 1mo ago

Data Science Specialist

Bellevue, Washington, United States

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Role Summary

We are looking for an experienced Data Professional to support Azure-based data platform, analytics, reporting, and advanced analytics and science work. The role requires hands-on experience in data engineering, data analysis, business intelligence, and Python-based analytics using Microsoft Azure technologies.

Key Responsibilities

  • Design, build, and maintain data pipelines using Azure Data Factory, Azure Data Lake Gen2, Synapse, Fabric, and Databricks.
  • Ingest, transform, and validate data from multiple business systems, APIs, databases, and files.
  • Develop and maintain data models, source-to-target mappings, business rules, and data quality checks.
  • Perform data profiling, reconciliation, gap analysis, and root-cause analysis.
  • Write complex SQL, Python, PySpark, and KQL queries for data processing and analysis.
  • Build and support Power BI dashboards, reports, semantic models, KPIs, and analytical datasets.
  • Apply statistical analysis, forecasting, anomaly detection, and basic machine learning techniques where needed.
  • Support Dev/UAT/Prod deployments, CI/CD, documentation, testing, and stakeholder sign-off.
  • Ensure data security, governance, RBAC, access control, and performance best practices.

Required Skills

  • 5+ years of experience in data engineering, data analytics, BI, or data science.
  • Strong hands-on experience with Azure data services.
  • Strong skills in SQL, Python, PySpark, Power BI, and data modeling.
  • Experience with ADF, ADLS Gen2, Synapse/Fabric, Databricks, dbt, or Airbyte.
  • Ability to translate business requirements into technical specifications, data models, reports, and analytics solutions.
  • Experience with data validation, documentation, UAT support, and stakeholder communication.
  • Knowledge of Azure DevOps, Git, CI/CD, and Agile delivery is preferred.

Qualifications

Bachelor’s or Master’s degree in Computer Science, Data Science, Information Technology, Engineering, Statistics, or a related field.

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