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Azure Data Engineer

5 - 8 years

15 - 20 Lacs

bengaluru

Posted:20 hours ago| Platform: Naukri logo

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Skills Required

azure data factory data bricks python sql pyspark

Work Mode

Hybrid

Job Type

Full Time

Job Description

Job Summary:

Role Overview We are seeking a hands-on Data Quality Analyst to strengthen the integrity and reliability of enterprise data assets. The ideal candidate will blend technical skills in Databricks, PySpark, and Python with practical experience using Ataccama for rule-based data quality management. This role sits at the intersection of data engineering, governance, and analyticsresponsible for implementing automated quality checks, root cause analysis, and continuous data improvement.

Key Responsibilities

1. Data Quality Development & Monitoring

  • Design and implement automated data quality rules and validation checks using

    Databricks (Delta Lake)

    and

    PySpark

    .
  • Build and operationalize data quality workflows in

    Ataccama ONE / Ataccama Studio

    .
  • Perform data profiling, anomaly detection, and reconciliation across systems and data sources.
  • Establish thresholds, KPIs, and alerts for data quality metrics.

2. Root Cause Analysis & Issue Management

  • Investigate data anomalies and quality incidents using SQL, Python, and Ataccama diagnostics.
  • Collaborate with data engineers and business analysts to identify and remediate root causes.
  • Document recurring data issues and contribute to preventive automation solutions.

3. Collaboration & Governance Support

  • Partner with data stewards, governance, and analytics teams to define and maintain DQ rules and SLAs.
  • Contribute to metadata enrichment, lineage documentation, and data catalog integration.
  • Support adoption of DQ frameworks and promote data reliability best practices.

4. Automation & Continuous Improvement

  • Integrate DQ validations into orchestration tools (Airflow, Databricks Workflows, or ADF).
  • Leverage

    Python/Pyspark

    libraries to complement existing platforms.
  • Propose process improvements to enhance automation, monitoring, and exception management.

DQ Tools: Ataccama DQMS, Informatica DQ, Collibra (basic exposure)

Data Platforms: Databricks, Azure Synapse, Snowflake

Data Engineering: SQL, PySpark, Python

Integration & Automation: Azure Data Factory, Airflow, APIs

Visualization & Monitoring: Power BI, ServiceNow (for issue tracking)

Governance & Quality Management: Metadata management, data lineage tracking, KPI reporting

Qualifications & Experience

  • Bachelors degree in Computer Science, Information Systems, Statistics, or related field.

  • 6–9 years of experience

    in data quality, data engineering, or analytics operations.
  • Strong command of

    SQL

    ,

    Python

    , and

    PySpark

    for data validation and troubleshooting.
  • Proven experience with

    Ataccama DQ rule creation and monitoring

    .
  • Hands-on exposure to

    Databricks

    for building and running data pipelines.
  • Working knowledge of reconciliation processes, data profiling, and DQ metrics.

Soft Skills & Attributes

  • Analytical thinker with strong problem-solving abilities.
  • Detail-oriented and methodical approach to troubleshooting.
  • Strong communication skills for cross-functional collaboration.
  • Proactive mindset, capable of owning issues through resolution.
  • Comfortable balancing hands-on technical work with business stakeholder interaction.

Preferred / Nice to Have

  • Exposure to data governance frameworks or MDM initiatives.
  • Familiarity with observability tools (Grafana, Datadog, Prometheus).
  • Understanding of CI/CD practices for data quality deployment.
  • Certification in Databricks, Ataccama, or a major cloud platform (Azure/AWS).

Success Indicators

  • Increase in automated data quality coverage across critical datasets.
  • Reduction in recurring manual DQ exceptions.
  • Improved timeliness and accuracy of data available for analytics.
  • Positive stakeholder feedback on data trust and reliability.

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