AWS Data Lead
AWS Data Lead
Location
In office - 5 days / Bangalore
Experience Range
6 to 10 years
Competency Area
AWS Data & Analytics
Role Summary
The AWS Data Lead will lead the design and delivery of AWS data engineering and analytics solutions, guide data engineers and analytics engineers, and ensure that data platforms are scalable, secure, governed, reliable, and aligned to business outcomes. The role bridges architecture and hands-on delivery across data ingestion, transformation, storage, analytics, governance, operational readiness, and AI / GenAI data foundations.
Job Role & Responsibilities
Lead discovery sessions and translate business, analytics, and AI requirements into an executable AWS data platform backlog.
Own technical delivery of data ingestion, ETL / ELT, data lake, warehouse, analytics, and streaming workstreams.
Define solution standards for pipeline design, data modeling, coding, testing, orchestration, monitoring, and deployment.
Guide and mentor data engineers and analytics engineers through design reviews, code reviews, and production-readiness checkpoints.
Collaborate with the AWS Data Architect on target architecture, service selection, scalability, resilience, security, and cost optimization.
Establish data quality rules, reconciliation controls, metadata, cataloging, lineage, and governance practices.
Coordinate with AI / ML and GenAI teams to prepare trusted datasets, feature pipelines, knowledge repositories, and RAG-ready data assets.
Manage technical risks, dependencies, defects, performance issues, and production incidents through root-cause analysis and corrective actions.
Support estimations, proposals, proof-of-concepts, customer demonstrations, and reusable AWS data accelerators when required.
Ensure documentation, knowledge transfer, and operational handover are complete for each release.
Specific Expertise Required
Should have worked on at least 1 project on AWS data migration.
Should have AWS certification relevant to the role.
Should be hands-on with ETL, Reporting, and Pipelines.
Should be able to propose/use solutions, approaches, and required tools for AWS data migration.
Required Primary Skills
AWS data platform delivery using Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, and AWS Lake Formation.
Strong SQL, Python, and PySpark skills for data ingestion, transformation, validation, and optimization.
ETL / ELT pipeline design, data lake and lakehouse implementation, data warehousing, and dimensional modeling.
Technical leadership for data engineering teams, solution reviews, sprint planning, and delivery governance.
Data quality, metadata, lineage, cataloging, security, privacy, and access-control implementation.
Secondary Skills
Amazon EMR, Amazon Kinesis, AWS DMS, AWS DataSync, and Amazon OpenSearch.
Streaming and event-driven data processing using Kinesis, Kafka, or equivalent technologies.
Infrastructure as Code using Terraform or AWS CloudFormation and CI/CD for data workloads.
Amazon QuickSight, Power BI, or Tableau integration for analytics consumption.
AI / GenAI data foundations including feature stores, embeddings, vector-ready datasets, and RAG data pipelines.
Technical / Functional Skills
AWS services: Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, AWS Lake Formation, Amazon Kinesis, AWS DMS, AWS DataSync, and Amazon OpenSearch.
Programming and data processing: SQL, Python, PySpark, Apache Spark, shell scripting, and Scala as an advantage.
Data architecture: data lakes, lakehouse patterns, enterprise data warehouses, batch and streaming pipelines, CDC, data marts, and semantic layers.
Databases: Amazon RDS, Aurora, DynamoDB, PostgreSQL, MySQL, and NoSQL platforms.
Engineering practices: Git, CI/CD, automated testing, monitoring, logging, performance tuning, cost optimization, and operational support.
Governance and security: IAM, encryption, Lake Formation permissions, data classification, lineage, auditability, and compliance controls.
Preferred Certifications
AWS Certified Data Engineer - Associate.
Databricks Data Engineer certification or equivalent data-platform certification (desirable).
Expected Deliverables
AWS data solution design and delivery plan.
Data ingestion, ETL / ELT, streaming, and orchestration pipelines.
Curated data lake, lakehouse, warehouse, and analytics-ready datasets.
Data models, interface specifications, mappings, and transformation rules.
Data quality, reconciliation, lineage, security, and governance documentation.
Code-review records, test evidence, performance tuning results, and release-readiness checklist.
Monitoring dashboards, operational runbooks, incident RCA, and handover documentation.
Reusable templates, reference patterns, accelerators, and lessons learned for the AWS competency.
Qualification
B.E. / B.Tech / M.Tech / MCA or equivalent degree in Computer Science, Information Technology, Data Engineering, Analytics, or a related discipline.
6 to 10 years of overall experience in data engineering or analytics, including hands-on AWS data-platform delivery and team leadership responsibilities.
Demonstrated experience delivering production-grade data pipelines, data lakes, warehouses, or analytics platforms.
Good to Have
Experience with migration from on-premises, Azure, GCP, Hadoop, Snowflake, or legacy warehouse platforms to AWS.
Exposure to AWS Well-Architected reviews, FinOps, data mesh, lakehouse, and domain-oriented data-product practices.
Experience building data foundations for SageMaker, Bedrock, recommendation systems, predictive analytics, or GenAI / RAG use cases.
Customer-facing consulting experience across discovery workshops, architecture reviews, estimations, proposals, and technical presentations.
Experience creating competency assets such as reference architectures, playbooks, reusable frameworks, and proof-of-concepts.