Talenthub

Hire remote software engineers for custom development

Data Engineer

Experience: 8–10 Years
Location: Remote / Hybrid / Onsite (as applicable)
Employment Type: Full-time / Contract

Job Summary :

We are looking for a highly skilled Data Engineer with 8–10 years of experience in designing, building, and optimizing data pipelines and architectures. The ideal candidate will have a strong background in ETL development, cloud data platforms (Azure/AWS/GCP), and big data frameworks. This role requires hands-on expertise in SQL, Python, Spark, and data modeling for scalable and efficient data processing solutions.

Key Responsibilities :

  • Design, develop, and maintain ETL/ELT data pipelines to collect, transform, and load structured and unstructured data.
  • Build and manage data integration workflows across multiple sources and targets using modern data engineering tools.
  • Develop and optimize data models, schemas, and data warehouse solutions (Snowflake, Redshift, Synapse, BigQuery, etc.).
  • Work extensively with big data frameworks such as Apache Spark, PySpark, Databricks, or similar technologies.
  • Implement and maintain data lakes and data warehouses on Azure, AWS, or GCP.
  • Collaborate with data analysts, scientists, and business stakeholders to define data requirements and enable analytics.
  • Ensure data quality, integrity, and security through monitoring, validation, and governance best practices.
  • Develop and manage data ingestion, transformation, and orchestration using tools like Airflow, Azure Data Factory, or AWS Glue.
  • Monitor data pipelines for performance and reliability, resolving production issues proactively.
  • Automate repetitive tasks and improve efficiency using Python or shell scripting.
  • Contribute to data architecture design, ensuring scalability and compliance with enterprise data standards.

Required Skills :

  • Languages: SQL, Python, PySpark
  • ETL/ELT Tools: Azure Data Factory, AWS Glue, Informatica, or similar
  • Big Data Frameworks: Apache Spark, Databricks, Hadoop ecosystem
  • Databases: SQL Server, Snowflake, Redshift, Synapse, BigQuery, or similar
  • Cloud Platforms: Azure, AWS, or GCP (Data Lake, S3, EC2, Lambda, Synapse, etc.)
  • Version Control: Git, CI/CD pipelines
  • Concepts: Data Modeling, Data Warehousing, Data Governance, and Performance Optimization

Nice to Have :

  • Experience with Kafka or other streaming platforms for real-time data ingestion.
  • Exposure to containerization (Docker, Kubernetes) and DevOps practices.
  • Knowledge of ML pipelines, feature engineering, or data science workflows.
  • Familiarity with infrastructure as code (Terraform, CloudFormation).
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