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𝐀𝐖𝐒 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫
(𝑎𝑙𝑠𝑜 ℎ𝑖𝑟𝑖𝑛𝑔 𝑓𝑜𝑟: 𝐴𝑊𝑆 𝐷𝑎𝑡𝑎 𝐸𝑛𝑔𝑖𝑛𝑒𝑒𝑟 / 𝐷𝑎𝑡𝑎 𝐿𝑎𝑘𝑒 𝐸𝑛𝑔𝑖𝑛𝑒𝑒𝑟)
𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞: 3 – 7 Years
𝐋𝐨𝐜𝐚𝐭𝐢𝐨𝐧: India
𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧: AWS Certification (Data Engineer / Solutions Architect / Big Data Specialty) preferred
𝐀𝐛𝐨𝐮𝐭 𝐔𝐬
Acon InfoPlus Ltd. is a data and cloud solutions company helping organizations modernize their data infrastructure across Snowflake, AWS, and Azure. We work with clients across industries — including life sciences — to build scalable, secure data pipelines and AI-enabled analytics solutions.
𝐀𝐛𝐨𝐮𝐭 𝐭𝐡𝐞 𝐑𝐨𝐥𝐞
We are looking for an experienced 𝐀𝐖𝐒 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 to design, build, and maintain a validated, multi-zone data lake architecture supporting clinical and life sciences data pipelines. The ideal candidate will have hands-on experience with modern lakehouse architectures (S3 + Iceberg), GxP-compliant data environments, and AWS-native governance and security tooling.
𝐑𝐞𝐪𝐮𝐢𝐫𝐞𝐝 𝐒𝐤𝐢𝐥𝐥𝐬 & 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞
- 𝟑–𝟕 𝐲𝐞𝐚𝐫𝐬 of experience as a Data Engineer, with significant hands-on work on 𝐀𝐖𝐒 𝐝𝐚𝐭𝐚 𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬.
- Strong experience with 𝐀𝐖𝐒 𝐒𝟑, 𝐀𝐖𝐒 𝐆𝐥𝐮𝐞 (𝐂𝐚𝐭𝐚𝐥𝐨𝐠 & 𝐄𝐓𝐋), 𝐋𝐚𝐤𝐞 𝐅𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧, 𝐀𝐭𝐡𝐞𝐧𝐚/𝐑𝐞𝐝𝐬𝐡𝐢𝐟𝐭, and other core AWS data services.
- Hands-on experience with 𝐀𝐩𝐚𝐜𝐡𝐞 𝐈𝐜𝐞𝐛𝐞𝐫𝐠 or similar open table formats (Delta Lake, Hudi) is highly desirable.
- Proficiency in 𝐏𝐲𝐭𝐡𝐨𝐧 𝐚𝐧𝐝/𝐨𝐫 𝐏𝐲𝐒𝐩𝐚𝐫𝐤, 𝐒𝐐𝐋 for building ETL/ELT pipelines.
- Understanding of 𝐦𝐞𝐝𝐚𝐥𝐥𝐢𝐨𝐧 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 (Bronze/Silver/Gold) design patterns.
- Experience with 𝐀𝐖𝐒 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 & 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐭𝐨𝐨𝐥𝐬: IAM, KMS, CloudTrail, and tag-based access control (LF-TBAC).
- Experience with 𝐂𝐈/𝐂𝐃 and infrastructure-as-code tools such as 𝐓𝐞𝐫𝐫𝐚𝐟𝐨𝐫𝐦 or CloudFormation.
- Experience with 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰 𝐨𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 𝐭𝐨𝐨𝐥𝐬 such as 𝐀𝐩𝐚𝐜𝐡𝐞 𝐀𝐢𝐫𝐟𝐥𝐨𝐰 (or AWS-native equivalents like MWAA/Step Functions).
𝐊𝐞𝐲 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬
- Design, develop, and maintain scalable 𝐝𝐚𝐭𝐚 𝐢𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 from multiple upstream sources into the AWS Data Lake.
- Build and manage data layers following a 𝐦𝐞𝐝𝐚𝐥𝐥𝐢𝐨𝐧 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 using 𝐒𝟑 𝐚𝐧𝐝 𝐀𝐩𝐚𝐜𝐡𝐞 𝐈𝐜𝐞𝐛𝐞𝐫𝐠 table formats.
- Manage and optimize the 𝐆𝐥𝐮𝐞 𝐃𝐚𝐭𝐚 𝐂𝐚𝐭𝐚𝐥𝐨𝐠 and enforce fine-grained access control using 𝐀𝐖𝐒 𝐋𝐚𝐤𝐞 𝐅𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 (𝐋𝐅-𝐓𝐁𝐀𝐂).
- Apply and maintain data 𝐭𝐚𝐠𝐠𝐢𝐧𝐠 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 (study, blinding tier, domain, validation status) for governance and access control.
- Ensure all pipelines and data zones comply with 𝐆𝐱𝐏, 𝟐𝟏 𝐂𝐅𝐑 𝐏𝐚𝐫𝐭 𝟏𝟏, 𝐚𝐧𝐝 𝐀𝐋𝐂𝐎𝐀+ data integrity principles, including audit trails and immutability requirements.
- Support the 𝐑𝐚𝐧𝐝𝐨𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧/𝐔𝐧𝐛𝐥𝐢𝐧𝐝𝐢𝐧𝐠 𝐳𝐨𝐧𝐞, ensuring restricted compute and access separation from standard pipelines.
- Configure and monitor 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐚𝐧𝐝 𝐜𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞 𝐜𝐨𝐧𝐭𝐫𝐨𝐥𝐬 including CloudTrail logging, KMS encryption, and Identity Center (SSO) role-based access mapped to Lake Formation roles.
- Collaborate with data governance, QA/validation, and compliance teams to support validated (GxP) and non-validated (exploratory analytics) environments within the same AWS ecosystem.
- Troubleshoot pipeline failures, optimize performance/cost of S3, Glue, and Iceberg-based workloads, and ensure high availability of data pipelines.
- Document data lineage, transformation logic, and pipeline architecture for audit and validation purposes.
𝐏𝐫𝐞𝐟𝐞𝐫𝐫𝐞𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬
- AWS Certified Data Engineer – Associate
- AWS Certified Solutions Architect – Associate/Professional
- AWS Certified Big Data / Analytics Specialty
𝐆𝐨𝐨𝐝 𝐭𝐨 𝐇𝐚𝐯𝐞
- Prior experience working with pharmaceutical, clinical trial, or life sciences data.
- Familiarity with 𝐫𝐞𝐠𝐮𝐥𝐚𝐭𝐞𝐝/𝐆𝐱𝐏 𝐝𝐚𝐭𝐚 𝐞𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭𝐬 and audit trail requirements (Part 11, ALCOA+).
- Experience supporting both validated (GxP) and non-validated analytics zones within the same platform.
- Experience with 𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞 for data warehousing/analytics, including integration with S3-based data lakes.
- Familiarity with 𝐝𝐛𝐭 for data transformation and modeling.
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