Description

- Own the end-to-end design and delivery of batch and near-real-time data pipelines on AWS.
- Translate business requirements into scalable conceptual, logical, and physical data models.
- Design robust data architectures using services such as Amazon S3, AWS Glue, Amazon Redshift, AWS Lambda,

Amazon EMR, Amazon Kinesis, and AWS Step Functions.

- Develop and optimize ETL/ELT workflows using Python, PySpark, SQL, AWS Glue, and orchestration tools such as

Amazon MWAA (Apache Airflow) or Step Functions.

- Build curated, reusable data layers and lakehouse patterns on Amazon S3 using open formats such as Parquet and table formats such as Apache Iceberg, where appropriate.
- Design and tune Amazon Redshift data models, distribution styles, sort keys, workload management, and queries for performance and cost efficiency.
- Implement data ingestion from databases, APIs, files, streaming platforms, and third-party sources using fit-for purpose AWS services.
- Create reusable frameworks for data validation, reconciliation, metadata capture, lineage, monitoring, and error handling.
- Implement Infrastructure as Code and automated deployment practices using AWS CloudFormation or Terraform,

integrated with CI/CD pipelines.

- Apply AWS security best practices across IAM, KMS encryption, VPC networking, Secrets Manager, Lake

Formation, and least-privilege access.

- Drive data quality, governance, retention, archival, and regulatory compliance across the data lifecycle.
- Lead technical design reviews, code reviews, production readiness assessments, and root-cause analysis for complex data issues.
- Mentor data engineers, establish engineering standards, and collaborate with product owners, analysts, data scientists, architects, and platform teams.

Enable Skills-Based Hiring No ECMS REQ IDAdditional Details

- ECMS REQ ID : 585079
- Project Location 1 : UP | Noida
- Project Location 2 : (No Value)
- Project Location 3 : (No Value)
- Project Location 4 : (No Value)
- Delivery SPOC : Chaitanya
- Relevant Experience :



7 to 10 Years
- Mandatory skills : • At least 7 years of relevant experience in data engineering, including strong hands-on delivery of enterprise-grade data platforms: substantial recent experience on AWS is expected. • Advanced hands-on experience with Amazon S3, AWS Glue, Amazon Redshift, AWS Lambda, IAM, CloudWatch, and one or more of Amazon EMR, Kinesis, DMS, Athena, or Step Functions. • Strong programming skills in Python and PySpark, with advanced SQL for data transformation, reconciliation, and performance tuning. • Demonstrated experience building scalable ETL/ELT frameworks, data lakes, lakehouses, and cloud data warehouses. • Strong knowledge of dimensional modeling, data warehousing, schema design, partitioning, file formats, and distributed data-processing concepts. • Experience with orchestration using Apache Airflow/Amazon MWAA, Step Functions, or an equivalent enterprise scheduler. • Experience implementing CI/CD, automated testing, version control, and Infrastructure as Code using Git, Terraform, CloudFormation, AWS CodePipeline, Jenkins, GitHub Actions, or similar tools. • Practical knowledge of data security, access control, encryption, secrets management, audit logging, and network isolation in AWS. • Experience with monitoring, alerting, incident resolution, capacity planning, and optimization of production data workloads. • Ability to communicate technical designs and trade-offs clearly to both engineering and non-technical stakeholders. • Proven ability to lead workstreams, mentor engineers, estimate delivery, manage dependencies, and deliver in Agile environments.




- Desired skills : • At least 7 years of relevant experience in data engineering, including strong hands-on delivery of enterprise-grade data platforms: substantial recent experience on AWS is expected. • Advanced hands-on experience with Amazon S3, AWS Glue, Amazon Redshift, AWS Lambda, IAM, CloudWatch, and one or more of Amazon EMR, Kinesis, DMS, Athena, or Step Functions. • Strong programming skills in Python and PySpark, with advanced SQL for data transformation, reconciliation, and performance tuning. • Demonstrated experience building scalable ETL/ELT frameworks, data lakes, lakehouses, and cloud data warehouses. • Solid knowledge of dimensional modeling, data warehousing, schema design, partitioning, file formats, and distributed data-processing concepts. • Experience with orchestration using Apache Airflow/Amazon MWAA, Step Functions, or an equivalent enterprise scheduler. • Experience implementing CI/CD, automated testing, version control, and Infrastructure as Code using Git, Terraform, CloudFormation, AWS CodePipeline, Jenkins, GitHub Actions, or similar tools. • Practical knowledge of data security, access control, encryption, secrets management, audit logging, and network isolation in AWS. • Experience with monitoring, alerting, incident resolution, capacity planning, and optimization of production data workloads. • Ability to communicate technical designs and trade-offs clearly to both engineering and non-technical stakeholders. • Proven ability to lead workstreams, mentor engineers, estimate delivery, manage dependencies, and deliver in Agile environments.
- Domain (Industry) : Oil and Gas
- BGC (Before onboarding / After onboarding) : PreOB
- Total Experience (Ex. 5-7 Years) : 10 Years
- BGC Details : Standard Infosys Checklist
- BGC Vendor : Any Nasscom Vendor
- Mode of Interview : Face to Face
- WFO / WFH / Hybrid : Hybrid
- Please enter shift timings : 9-6
- Shift Timings : General

📌 585079 | IC | Senior AWS Data Engineer | INDIA
🏢 Trigent Software
📍 India