Aws Data Engineer

Paltech Consulting

Hyderabad

Hybrid

INR 1,400,000 - 2,100,000

Full time

4 days ago
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Qualifications

  • 48 years of Data Engineering experience.
  • Strong hands-on experience with Amazon Redshift.
  • Strong SQL skills.
  • Hands-on experience with AWS Glue.
  • Strong experience with Amazon S3.
  • Experience with PySpark / Apache Spark.
  • Strong understanding of ETL/ELT architecture.
  • Experience with incremental loading and data-quality validation.
  • Good understanding of cloud data warehouse concepts.

Responsibilities

  • Design, develop and maintain ETL/ELT pipelines using AWS services.
  • Build and optimize data pipelines using AWS Glue, S3 and PySpark.
  • Develop and maintain Amazon Redshift data warehouse solutions.
  • Load and transform data from S3 and various source systems into Redshift.
  • Write complex and optimized SQL queries, stored procedures and data transformations.
  • Design appropriate Redshift distribution keys and sort keys.
  • Perform Redshift query and workload optimization, including identifying data skew, inefficient joins and unnecessary data scans.
  • Implement incremental loads, CDC and data-quality checks.
  • Work with Redshift Spectrum for querying data directly from S3 where appropriate.
  • Implement data partitioning and file-format optimization, preferably using Parquet.
  • Develop scalable PySpark transformations for large datasets.
  • Implement monitoring, logging and failure/retry mechanisms for production pipelines.
  • Work with IAM, CloudWatch, Lambda/EventBridge and other AWS services as required.
  • Participate in CI/CD and deployment automation for data pipelines.
  • Troubleshoot production data pipeline and Redshift performance issues.

Skills

Amazon Redshift
SQL
AWS Glue
S3
PySpark / Apache Spark
ETL/ELT architecture
Incremental loading
Data-quality validation
Query optimization
Redshift Spectrum

Tools

Parquet
IAM
CloudWatch
Lambda
EventBridge
Athena
EMR

Job description

Job Description AWS Data Engineer (Redshift)

Location: Hyderabad | Work From Office

Experience: 48 Years

Notice Period: Immediate to 30 Days preferred

We are looking for an experienced AWS Data Engineer with strong hands-on experience in Amazon Redshift, AWS Glue, S3, SQL and PySpark to design and build scalable data pipelines and cloud data warehouse solutions.

Key Responsibilities
  • Design, develop and maintain ETL/ELT pipelines using AWS services.
  • Build and optimize data pipelines using AWS Glue, S3 and PySpark.
  • Develop and maintain Amazon Redshift data warehouse solutions.
  • Load and transform data from S3 and various source systems into Redshift.
  • Write complex and optimized SQL queries, stored procedures and data transformations.
  • Design appropriate Redshift distribution keys and sort keys.
  • Perform Redshift query and workload optimization, including identifying data skew, inefficient joins and unnecessary data scans.
  • Implement incremental loads, CDC and data-quality checks.
  • Work with Redshift Spectrum for querying data directly from S3 where appropriate.
  • Implement data partitioning and file-format optimization, preferably using Parquet.
  • Develop scalable PySpark transformations for large datasets.
  • Implement monitoring, logging and failure/retry mechanisms for production pipelines.
  • Work with IAM, CloudWatch, Lambda/EventBridge and other AWS services as required.
  • Participate in CI/CD and deployment automation for data pipelines.
  • Troubleshoot production data pipeline and Redshift performance issues.
Required Skills
  • 48 years of Data Engineering experience.
  • Strong hands-on experience with Amazon Redshift.
  • Strong SQL skills.
  • Hands-on experience with AWS Glue.
  • Strong experience with Amazon S3.
  • Experience with PySpark / Apache Spark.
  • Strong understanding of ETL/ELT architecture.
  • Experience with incremental loading and data-quality validation.
  • Good understanding of cloud data warehouse concepts.
Redshift-specific expertise
  • COPY command and S3-to-Redshift loading.
  • DISTKEY / SORTKEY selection.
  • Distribution styles.
  • Query optimization.
  • Data skew.
  • VACUUM / ANALYZE concepts.
  • Redshift Spectrum.
  • Workload management.
  • Large-volume data loading and optimization.
Good to Have
  • AWS Lambda
  • EventBridge
  • CloudWatch
  • Athena
  • EMR
  • IAM
  • Airflow / MWAA
  • dbt
  • Terraform
  • CI/CD
  • Python
Ideal Candidate

We are looking for someone who has genuine hands-on Redshift experience, not just Redshift listed in the resume. The candidate should be able to explain a complete architecture such as:

Source Systems S3 AWS Glue/PySpark Redshift BI/Analytics

and confidently troubleshoot Redshift performance, data loading, distribution, sorting and SQL optimization.

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