Data Engineer

We are looking for a Data Engineer with 2–5 years of experience to design, build, and maintain reliable data pipelines and analytics-ready datasets. The ideal candidate will have strong SQL and Python or PySpark skills, hands-on experience with cloud data platforms, and the ability to troubleshoot data-quality, performance, and production issues independently.
Key Responsibilities

·    Design, develop, and maintain scalable ETL and ELT pipelines for batch and scheduled data processing.

·    Build and optimize data transformations using SQL, Python, and PySpark.

·    Ingest and integrate data from databases, APIs, files, SharePoint, and cloud storage.

·    Create and maintain curated, analytics-ready datasets for Power BI, Superset, and other reporting platforms.

·    Implement data-quality checks, reconciliation controls, monitoring, logging, and failure alerts.

·    Troubleshoot pipeline failures, schema mismatches, performance bottlenecks, and data discrepancies.

·    Optimize large-scale data processing for runtime, memory, storage, and cost efficiency.

·    Automate recurring workflows and schedule production jobs using orchestration tools such as Apache Airflow.

·    Maintain technical documentation, data mappings, business rules, and operational runbooks.

·    Collaborate with BI analysts, application teams, and business stakeholders to deliver dependable data solutions.

Requirements

Required Skills and Experience

·    2–5 years of professional experience in data engineering, ETL development, or a similar role.

·    Strong SQL skills, including joins, common table expressions, window functions, data validation, and query optimization.

·    Hands-on experience with Python and/or PySpark for data processing and automation.

·    Practical experience building and supporting ETL or ELT pipelines in production.

·    Experience working with large datasets, incremental loads, upserts, schema changes, and partitioned data.

·    Good understanding of relational databases, dimensional modeling, data warehousing, and file formats such as Parquet, CSV, and JSON.

·    Experience with Git or another version-control system and structured deployment practices.

·    Strong debugging, analytical, documentation, and stakeholder-communication skills.
Preferred Skills

·    Experience with AWS services such as Amazon S3, Athena, EMR, and EMR Serverless.

·    Experience with Apache Spark, Delta Lake, Apache Airflow, Dremio, or Trino-compatible SQL engines.

·    Familiarity with Power BI, Apache Superset, or other downstream reporting tools.

·    Experience with REST APIs, SharePoint integrations, and automated file-delivery workflows.

·    Understanding of data security, access controls, privacy, and production monitoring practices.

Benefits

* 5 Days working
* Remote Job
* Medical Insurance