Sr Data Engineer

Job Description – Data Engineer Experience:

5–7 Years Role:

Sr. Data Engineer Department:

Data Engineering / Product Engineering Role Overview We are looking for an experienced

Data Engineer

with 5–7 years of hands-on experience in

ETL/ELT, database engineering, Big Data processing, and complex bi-directional data integrations . The candidate should have strong SQL and database expertise, experience working with large datasets, and the ability to design, develop, optimize, and support reliable data pipelines in production environments. Key Responsibilities Design, develop, and maintain scalable

ETL/ELT pipelines

for batch and near-real-time data processing. Build data integrations using

REST APIs, SFTP, files, webhooks, and database-to-database interfaces . Develop and support

bi-directional data integrations

between applications, databases, and external enterprise systems. Implement data synchronization, incremental processing, reconciliation, error handling, retries, and duplicate/missing-data prevention. Develop complex

SQL queries, stored procedures, functions, views, and data transformations . Work extensively with

PostgreSQL and SQL Server ; exposure to MongoDB is preferred. Perform

database and query performance tuning , including execution-plan analysis, indexing, joins, partitioning, locking/blocking, and long-running query optimization. Work with large datasets and distributed processing frameworks; optimize data processing for scalability and performance. Implement data validation, quality checks, logging, auditing, and monitoring for critical pipelines. Troubleshoot production ETL, database, integration, and data-quality issues and perform root-cause analysis. Collaborate with application, product, DBA, DevOps, and client teams to resolve end-to-end data issues. Required Technical Skills Must Have: 5–7 years of Data Engineering / ETL experience Strong

SQL and Python

skills Strong hands-on experience with

PostgreSQL and SQL Server Good understanding of

ETL/ELT and data pipeline architecture Experience with

complex data integrations and data synchronization Experience with REST APIs, SFTP, file-based and database integrations Strong understanding of

database performance tuning and optimization Experience handling large-volume datasets Good understanding of data modeling, data quality, validation, and reconciliation Strong troubleshooting and problem-solving skills Good to Have: Apache Spark / PySpark Apache Airflow Talend / SSIS / other ETL tools MongoDB Azure / AWS / GCP Azure Databricks / Delta Lake / Data Lake Kafka or other streaming technologies CDC / incremental data processing Git, Azure DevOps/GitHub and CI/CD Preferred Candidate Profile The ideal candidate should be comfortable working across the complete data lifecycle: Candidates who have experience working with

multiple databases, high-volume data, complex bi-directional integrations, and performance-critical production workloads

will be preferred. Key Competencies Strong analytical and problem-solving ability Good understanding of data architecture and integration patterns Ability to independently troubleshoot complex data issues Ownership of deliverables and production issues Good communication and cross-functional collaboration skills Education:

Bachelor's/Master's degree in Computer Science, IT, Engineering, or related technical discipline.