Senior Data Quality Engineer
Python
Amazon Web Services (AWS)Role Overview
We are looking for a Senior Data Quality Engineer who is passionate about building reliable and scalable data platforms. In this role, you will ensure high-quality, trustworthy data across pipelines and analytics systems by designing robust data ingestion frameworks, implementing data quality checks, and optimizing data transformations.
You will work closely with data engineers, analytics teams, and product stakeholders to ensure data accuracy, consistency, and reliability across the organization.
Key Responsibilities
- Cleanse, normalize, and enhance data quality across operational systems and new data sources flowing through the data platform.
- Design, build, monitor, and maintain ETL/ELT pipelines using Python, SQL, and Airflow.
- Develop and optimize data models, tables, and transformations in Snowflake.
- Build and maintain data ingestion workflows, including API integrations, file ingestion, and database connectors.
- Ensure data reliability, integrity, and performance across pipelines.
- Perform comprehensive data profiling to understand data structures, detect anomalies, and resolve inconsistencies.
- Implement data quality validation frameworks and automated checks across pipelines.
- Use data integration and data quality tools such as Deequ, Great Expectations (GX), Splink, Fivetran, Workato, Informatica, etc., to onboard new data sources.
- Troubleshoot pipeline failures and implement data monitoring and alerting mechanisms.
- Collaborate with engineering, analytics, and product teams in an Agile development environment.
Required Technical Skills
Core Technologies
- Strong hands-on experience with SQL
- Python for data transformation and pipeline development
- Workflow orchestration using Apache Airflow
- Experience working with Snowflake data warehouse
Data Engineering Expertise
- Strong understanding of ETL / ELT pipeline design
- Data profiling and data quality validation techniques
- Experience building data ingestion pipelines from APIs, files, and databases
- Data modeling and schema design
Tools & Platforms
- Data Quality Tools: Deequ, Great Expectations (GX), Splink
- Data Integration Tools: Fivetran, Workato, Informatica
- Cloud Platforms: AWS (preferred)
- Version Control & DevOps: Git, CI/CD pipelines
Qualifications
- 5–8 years of experience in Data Quality Engineering / Data Engineering
- Strong expertise in SQL, Python, Airflow, and Snowflake
- Experience working with large-scale datasets and distributed data systems
- Solid understanding of data engineering best practices across the development lifecycle
- Experience working in Agile environments (Scrum, sprint planning, etc.)
- Strong analytical and problem-solving skills
What We Look For
- Passion for data accuracy, reliability, and governance
- Ability to identify and resolve complex data issues
- Strong collaboration skills across data, engineering, and analytics teams
- Ownership mindset and attention to data integrity and performance
Why Join Us
- Opportunity to work on modern data platforms and large-scale datasets
- Collaborate with high-performing data and engineering teams
- Exposure to cloud data architecture and modern data tools
- Competitive compensation and strong career growth opportunities
Shubham Vishwakarma
Full Stack Developer - Averlon
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Full Stack Developer - Averlon