Data Quality Engineer / Data Analytics Engineer

Position Overview

We are seeking a highly skilled Data Quality Engineer / Data Analytics Engineer to support enterprise-wide data quality, governance, and analytics initiatives. The ideal candidate will have strong expertise in data profiling, cleansing, SQL development, ETL/ELT processes, cloud data platforms, and modern data engineering practices. This role requires someone who can thrive in environments with evolving or low data maturity and help establish scalable data quality frameworks, governance standards, and monitoring processes.

The candidate will work closely with data engineers, analysts, business stakeholders, and analytics teams to ensure data integrity, reliability, and readiness for reporting, advanced analytics, and AI/ML initiatives.

Key Responsibilities

Data Quality & Governance

• Analyze enterprise datasets to identify anomalies, duplicates, outliers, inconsistencies, and missing values.
• Design and implement scalable data quality frameworks, validation rules, and monitoring processes.
• Establish data quality metrics including completeness, accuracy, consistency, timeliness, and integrity.
• Perform root cause analysis for data issues and coordinate remediation efforts across systems and pipelines.
• Document data quality standards, governance practices, and issue-resolution procedures.

Data Engineering & Analytics

• Develop and optimize complex SQL queries, stored procedures, and data transformation logic.
• Build, maintain, and support ETL/ELT pipelines using SSIS or comparable technologies.
• Utilize Python for automation, validation scripts, data processing, and pipeline integration.
• Support data warehousing, lakehouse, and modern cloud-based architectures.
• Collaborate with analytics and data science teams to ensure high-quality datasets for reporting and AI/ML workloads.

Cloud & Modern Data Platforms

• Work with cloud ecosystems such as AWS, Azure, or GCP.
• Support modern data platforms including Snowflake, BigQuery, Databricks, or similar technologies.
• Implement scalable solutions using Spark, Kafka/Kinesis, Hadoop, and S3-based ecosystems.
• Utilize data profiling and governance tools such as Microsoft Purview and related observability platforms.

Collaboration & Communication

• Partner with business stakeholders, engineering teams, and analysts to gather requirements and improve data processes.
• Participate in Agile delivery processes using collaboration tools such as Jira.
• Provide technical guidance and recommendations for improving enterprise data quality maturity.

Required Qualifications

• Bachelor’s degree in Computer Science, Information Systems, Data Analytics, or related field.
• 8+ years of experience in data quality engineering, data analytics, or data engineering roles.
• Strong experience working in low-maturity or evolving data environments.
• Advanced SQL expertise for querying, validation, and transformation.
• Hands-on experience with ETL/ELT tools such as SSIS or equivalent platforms.
• Strong Python programming skills for automation and data validation.
• Experience with data profiling, cleansing, and validation processes.
• Solid understanding of data modeling, data warehouses, data lakes, and lakehouse concepts.
• Experience implementing enterprise data quality standards and governance practices.
• Hands-on experience with cloud platforms such as AWS, Azure, or GCP.
• Experience with Snowflake, BigQuery, Spark, Kafka/Kinesis, Hadoop, and S3 ecosystems.

Preferred Qualifications

• Experience with Microsoft Purview, Microsoft Fabric, Power BI, and Azure Key Vault.
• Knowledge of DAMA-DMBOK, DCAM, MDM concepts, and enterprise governance frameworks.
• Familiarity with AI/ML data readiness, feature engineering support, and analytics pipelines.
• Exposure to Databricks and modern cloud-native data engineering frameworks.
• Master’s degree preferred.

Preferred Certifications

• DAMA Certified Data Management Professional (CDMP)
• EDM Council DCAM Certification
• ASQ Data Quality Certification
• Collibra Data Steward Certification
• Certified Data Steward (eLearningCurve)
• Cloud Certifications (Azure, AWS, GCP, Databricks, AI/ML)

Technical Environment

• SQL, Python, PySpark, Spark
• SSIS, ETL/ELT Pipelines
• Snowflake, BigQuery
• AWS, Azure, GCP
• Hadoop, Kafka, Kinesis, S3
• Microsoft Purview, Fabric, Power BI
• Jira and enterprise collaboration tools