Join Solutionara as a Data Engineer to build enterprise data pipelines and ensure data quality across integrations. Ideal for engineers who enjoy both building and investigating.

About the Company

Solutionara Private Limited is an AI-led IT Consulting and Technology Services organization that delivers enterprise transformation through Cloud, Integration, Data Engineering, Digital Experience, AI, and Enterprise Applications. Partnering with enterprises across retail, manufacturing, distribution, and consumer goods, Solutionara helps design, implement, and support scalable technology solutions. As a rapidly growing consulting organization, it fosters a culture of ownership, innovation, collaboration, and continuous improvement. Every team member is expected to think like a consultant—understanding client objectives, challenging assumptions, leveraging AI responsibly, and continuously improving quality and productivity.

Role Overview

Solutionara is seeking a hands-on Data Engineer – Integration & Data Quality to develop enterprise data pipelines while ensuring data accuracy, completeness, consistency, and reliability across systems. This role blends traditional data engineering with data-quality engineering and is ideal for someone who enjoys investigating data problems as much as building pipelines.

Key Responsibilities

Data Engineering & Integration

• Develop, test, and maintain ETL/ELT pipelines.
• Ingest data from databases, APIs, flat files, cloud storage, and enterprise applications.
• Build data transformations using SQL, Python, and relevant technologies.
• Implement source-to-target mappings and support batch/incremental processing.
• Build reusable components for ingestion, transformation, and validation.
• Monitor pipelines and troubleshoot failures.

Data Quality

• Perform data profiling to identify anomalies, inconsistencies, duplicates, and missing data.
• Develop automated data-quality rules and validation processes.
• Implement cleansing and standardization logic.
• Perform source-to-target reconciliation and validate referential integrity.
• Develop duplicate-detection and exception-handling processes.
• Conduct root-cause analysis for recurring data-quality issues.
• Work with stakeholders to translate business rules into executable validations.

Engineering & Delivery

• Write clean, maintainable, testable code and participate in peer code reviews.
• Develop unit and data-validation tests.
• Follow engineering, security, and documentation standards.
• Collaborate with senior engineers, application teams, QA, BI, and client stakeholders.
• Document mappings, transformation rules, and configurations.
• Proactively raise data anomalies, technical risks, and dependencies.

Consulting & AI-Led Working

• Understand the business context of data processing.
• Question inconsistencies and use AI tools responsibly for development and analysis.
• Validate AI-generated outputs and automate repetitive tasks.

Tech Stack

• Languages & Databases: SQL, Python, relational databases (e.g., PostgreSQL, MySQL)
• Cloud Platforms: AWS (Glue, Redshift), Azure (ADF, Synapse)
• Data Engineering Tools: ETL/ELT pipelines, Databricks, Snowflake, dbt, Airflow
• Integration: APIs, flat files, cloud storage (S3, ADLS)
• Data Quality: Profiling, validation rules, reconciliation, cleansing
• Other: Source-to-target mapping, data modeling, version control (Git)

3–6 years in data engineering, ETL development, or related areas. Strong SQL and Python proficiency. Experience with APIs, databases, cloud storage, data validation, and reconciliation. Familiarity with AWS or Azure.

Competitive salary, exposure to cutting-edge cloud and AI tools, collaborative consulting culture, opportunities for ownership and growth.