Solutionara is hiring a hands-on Data Engineer – Integration & Data Quality to develop enterprise data pipelines while ensuring that data moving between systems is accurate, complete, consistent, and reliable.
This role combines traditional data engineering with data-quality engineering and is ideal for someone who enjoys investigating data problems as much as building pipelines.
Location: Noida, India (on-site). Experience: 3–6 years. Employment: full-time.
About Solutionara
Solutionara is an AI-led IT Consulting and Technology Services organization delivering enterprise transformation through Cloud, Integration, Data Engineering, Digital Experience, AI, and Enterprise Applications. Headquartered in the United States with a Global Delivery Center in Noida, India, we partner with enterprise organizations across retail, manufacturing, distribution, and consumer goods. Every team member is expected to think like a consultant and business problem solver. AI is embedded into how we work, but it is an enabler, not a replacement for expertise, critical thinking, or accountability.
What You’ll Do
Develop, test, and maintain ETL/ELT pipelines using SQL, Python, and relevant data-engineering technologies.
Ingest data from databases, APIs, flat files, cloud storage, and enterprise applications.
Implement source-to-target mappings and support batch and incremental data-processing patterns.
Build reusable components for ingestion, transformation, and validation, and support data migration and integration activities.
Monitor pipelines and troubleshoot processing failures.
Profile data and identify anomalies, inconsistencies, duplicates, and missing data.
Develop automated data-quality rules, cleansing and standardization logic, duplicate detection, and exception handling.
Perform source-to-target reconciliation and validate referential integrity across datasets.
Perform root-cause analysis for recurring data-quality problems and maintain data-quality logs and issue records.
Translate business rules into executable validations with business and technical stakeholders.
Write clean, testable code, develop unit and data-validation tests, take part in peer code reviews, and document mappings and rules.
Use AI-assisted tools responsibly and validate AI-generated outputs rather than treating them as authoritative.
Required Qualifications
3–6 years of experience in data engineering, ETL development, data integration, or related areas.
Strong SQL skills and working proficiency in Python.
Experience developing ETL/ELT pipelines and working with relational databases.
Experience integrating data through APIs, databases, files, or cloud storage.
Understanding of data modeling and source-to-target mapping.
Experience with data validation, profiling, cleansing, or reconciliation.
Familiarity with AWS, Azure, or modern cloud data platforms.
Strong analytical and troubleshooting skills.
Preferred Qualifications
Experience with Databricks, Snowflake, Redshift, Synapse, Glue, ADF, Airflow, dbt, or similar technologies.
Experience with enterprise application data, including ERP, CRM, PIM, PLM, or related platforms.
Experience working with product, customer, supplier/vendor, material, location, or reference data.
Exposure to manufacturing, retail, distribution, or consumer-goods environments.
Experience in consulting or client-facing technology delivery.
What Success Looks Like
Data pipelines operate reliably and produce trusted outputs.
Data-quality problems are detected early rather than discovered downstream.
Validation and reconciliation are automated wherever practical.
Data issues are investigated to root cause.
Engineering deliverables meet technical standards and business requirements.
You progressively take ownership of larger and more complex engineering components.
AWS