Meaningful work,
meaningful life

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.

Core Technical Skills

Category

Tools / Skills

Data Engineering & Quality

Databricks (Delta Lake), PySpark, SQL, Python

DQ Platforms

Ataccama ONE / Studio (DQ rules, workflows, profiling)

Orchestration

Apache Airflow, Azure Data Factory, or Databricks Jobs

Data Warehouses

Databricks Lakehouse

Version Control / CI-CD

Git, GitHub Actions, Azure DevOps

Data Catalog / Lineage (Optional)

Collibra, Alation, Ataccama Catalog

Cloud Environments

Azure (preferred), AWS, or GCP

Qualifications & Experience

• Bachelor’s 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.