What are the responsibilities and job description for the Senior Data Engineer position at 7Th Sky Tech?
Senior Data Engineer – 8 Years Experience
Job Type: Full-Time
Work Model: Hybrid
Experience: 8 Years
Employment: Full-Time
Job Summary
We are looking for an experienced Senior Data Engineer with 8 years of hands-on experience designing, developing, and maintaining scalable data platforms and data pipelines. The ideal candidate will have strong expertise in Python, SQL, ETL/ELT, cloud platforms, data warehousing, and distributed data processing.
The candidate will work closely with Data Scientists, Software Engineers, Business Analysts, and other stakeholders to build reliable, high-performance data solutions and enable data-driven decision-making.
Key Responsibilities
Languages: Python, SQL, Scala/Java
Big Data: Apache Spark, PySpark, Hadoop
Cloud: AWS / Azure / GCP
Data Warehousing: Snowflake, Redshift, Synapse, BigQuery
Databases: SQL Server, PostgreSQL, Oracle, MySQL
ETL/ELT: Databricks, AWS Glue, Azure Data Factory
Streaming: Kafka, Spark Streaming
Orchestration: Apache Airflow
DevOps: Git, Jenkins/GitHub Actions, Docker, CI/CD
Methodology: Agile/Scrum
Work Arrangement
This is a Full-Time Hybrid position. The selected candidate will be expected to work both remotely and from the designated office location based on business requirements.
What We’re Looking For
We are seeking a senior-level engineer who can independently own data engineering projects, contribute to architecture and design decisions, build production-grade pipelines, and collaborate effectively with technical and business teams.
Skills: data,pipelines,sql,platforms,aws,processing,cloud,etl,azure,apache
Job Type: Full-Time
Work Model: Hybrid
Experience: 8 Years
Employment: Full-Time
Job Summary
We are looking for an experienced Senior Data Engineer with 8 years of hands-on experience designing, developing, and maintaining scalable data platforms and data pipelines. The ideal candidate will have strong expertise in Python, SQL, ETL/ELT, cloud platforms, data warehousing, and distributed data processing.
The candidate will work closely with Data Scientists, Software Engineers, Business Analysts, and other stakeholders to build reliable, high-performance data solutions and enable data-driven decision-making.
Key Responsibilities
- Design, develop, and maintain scalable ETL/ELT data pipelines.
- Build and optimize data processing workflows using Python and SQL.
- Develop data ingestion solutions from databases, APIs, files, and other sources.
- Design and maintain enterprise data warehouses, data lakes, and lakehouse architectures.
- Work with large datasets using distributed processing technologies such as Apache Spark/PySpark.
- Develop data models and optimize complex SQL queries for performance.
- Implement data quality, validation, monitoring, and error-handling frameworks.
- Integrate data from structured and unstructured sources.
- Build and maintain batch and real-time/streaming data pipelines.
- Work with cloud data platforms such as AWS, Azure, or GCP.
- Implement CI/CD and DevOps practices for data engineering workflows.
- Collaborate with cross-functional teams to understand business and technical requirements.
- Troubleshoot pipeline failures, performance issues, and data quality problems.
- Ensure data security, governance, scalability, and compliance.
- Mentor junior and mid-level data engineers and contribute to technical architecture decisions.
- Document data pipelines, architectures, processes, and technical solutions.
- 8 years of professional experience in Data Engineering or related roles.
- Strong programming experience with Python.
- Advanced SQL skills with experience in query optimization.
- Strong experience with ETL/ELT development and data pipeline architecture.
- Hands-on experience with Apache Spark / PySpark.
- Experience with cloud platforms such as:
- AWS – S3, Glue, Redshift, EMR, Lambda
- Azure – Data Factory, Databricks, Synapse, ADLS
- GCP – BigQuery, Dataflow, Cloud Storage
- Strong understanding of data warehousing and dimensional data modeling.
- Experience with relational databases such as SQL Server, PostgreSQL, Oracle, or MySQL.
- Experience with Databricks and/or Snowflake is highly desirable.
- Knowledge of Apache Kafka or other streaming technologies.
- Experience with workflow orchestration tools such as Airflow, Azure Data Factory, or AWS Glue.
- Familiarity with Git, CI/CD, Docker, and DevOps practices.
- Strong understanding of data quality, governance, security, and metadata management.
- Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or a related field.
- Experience designing enterprise-scale data architectures.
- Experience with real-time data processing and streaming.
- Experience with Terraform or Infrastructure as Code.
- Familiarity with Kubernetes and containerized workloads.
- Experience working in Agile/Scrum environments.
- Strong communication, analytical, and problem-solving skills.
Languages: Python, SQL, Scala/Java
Big Data: Apache Spark, PySpark, Hadoop
Cloud: AWS / Azure / GCP
Data Warehousing: Snowflake, Redshift, Synapse, BigQuery
Databases: SQL Server, PostgreSQL, Oracle, MySQL
ETL/ELT: Databricks, AWS Glue, Azure Data Factory
Streaming: Kafka, Spark Streaming
Orchestration: Apache Airflow
DevOps: Git, Jenkins/GitHub Actions, Docker, CI/CD
Methodology: Agile/Scrum
Work Arrangement
This is a Full-Time Hybrid position. The selected candidate will be expected to work both remotely and from the designated office location based on business requirements.
What We’re Looking For
We are seeking a senior-level engineer who can independently own data engineering projects, contribute to architecture and design decisions, build production-grade pipelines, and collaborate effectively with technical and business teams.
Skills: data,pipelines,sql,platforms,aws,processing,cloud,etl,azure,apache
Salary : $63 - $67