Lead II - Data Engineering

Global Digital Transformation Solutions Provider

Lead II - Data Engineering

Peak Hire Solutions

Company

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Lead II - Data Engineering

at Global Digital Transformation Solutions Provider

Agency job

via Peak Hire Solutions

7 - 15 yrs

₹15L - ₹30L / yr

Bengaluru (Bangalore), Hyderabad, Noida, Mumbai, Navi Mumbai, Ahmedabad, Chennai, Coimbatore, Gurugram, Kochi (Cochin), Kolkata, Calcutta, Pune, Thiruvananthapuram, Trivandrum

Skills

Amazon Web Services (AWS)

Python

Data Lake

SENIOR DATA ENGINEER:

ROLE SUMMARY:

Own the design and delivery of petabyte-scale data platforms and pipelines across AWS and modern Lakehouse stacks. You’ll architect, code, test, optimize, and operate ingestion, transformation, storage, and serving layers. This role requires autonomy, strong engineering judgment, and partnership with project managers, infrastructure teams, testers, and customer architects to land secure, cost-efficient, and high-performing solutions.

RESPONSIBILITIES:

Architecture and design: Create HLD/LLD/SAD, source–target mappings, data contracts, and optimal designs aligned to requirements.
Pipeline development: Build and test robust ETL/ELT for batch, micro-batch, and streaming across RDBMS, flat files, APIs, and event sources.
Performance and cost tuning: Profile and optimize jobs, right-size infrastructure, and model license/compute/storage costs.
Data modeling and storage: Design schemas and SCD strategies; manage relational, NoSQL, data lakes, Delta Lakes, and Lakehouse tables.
DevOps and release: Establish coding standards, templates, CI/CD, configuration management, and monitored release processes.
Quality and reliability: Define DQ rules and lineage; implement SLA tracking, failure detection, RCA, and proactive defect mitigation.
Security and governance: Enforce IAM best practices, retention, audit/compliance; implement PII detection and masking.
Orchestration: Schedule and govern pipelines with Airflow and serverless event-driven patterns.
Stakeholder collaboration: Clarify requirements, present design options, conduct demos, and finalize architectures with customer teams.
Leadership: Mentor engineers, set FAST goals, drive upskilling and certifications, and support module delivery and sprint planning.

REQUIRED QUALIFICATIONS:

Experience: 15+ years designing distributed systems at petabyte scale; 10+ years building data lakes and multi-source ingestion.
Cloud (AWS): IAM, VPC, EC2, EKS/ECS, S3, RDS, DMS, Lambda, CloudWatch, CloudFormation, CloudTrail.
Programming: Python (preferred), PySpark, SQL for analytics, window functions, and performance tuning.
ETL tools: AWS Glue, Informatica, Databricks, GCP DataProc; orchestration with Airflow.
Lakehouse/warehousing: Snowflake, BigQuery, Delta Lake/Lakehouse; schema design, partitioning, clustering, performance optimization.
DevOps/IaC: Terraform with 15+ years of practice; CI/CD (GitHub Actions, Jenkins) with 10+ years; config governance and release management.
Serverless and events: Design event-driven distributed systems on AWS.
NoSQL: 2–3 years with DocumentDB including data modeling and performance considerations.
AI services: AWS Entity Resolution, AWS Comprehend; run custom LLMs on Amazon SageMaker; use LLMs for PII classification.

NICE-TO-HAVE QUALIFICATIONS:

Data governance automation: 10+ years defining audit, compliance, retention standards and automating governance workflows.
Table and file formats: Apache Parquet; Apache Iceberg as analytical table format.
Advanced LLM workflows: RAG and agentic patterns over proprietary data; re-ranking with index/vector store results.
Multi-cloud exposure: Azure ADF/ADLS, GCP Dataflow/DataProc; FinOps practices for cross-cloud cost control.

OUTCOMES AND MEASURES:

Engineering excellence: Adherence to processes, standards, and SLAs; reduced defects and non-compliance; fewer recurring issues.
Efficiency: Faster run times and lower resource consumption with documented cost models and performance baselines.
Operational reliability: Faster detection, response, and resolution of failures; quick turnaround on production bugs; strong release success.
Data quality and security: High DQ pass rates, robust lineage, minimal security incidents, and audit readiness.
Team and customer impact: On-time milestones, clear communication, effective demos, improved satisfaction, and completed certifications/training.

LOCATION AND SCHEDULE:

● Location: Outside US (OUS).

● Schedule: Minimum 6 hours of overlap with US time zones.

SENIOR DATA ENGINEER:

ROLE SUMMARY:

RESPONSIBILITIES:

Architecture and design: Create HLD/LLD/SAD, source–target mappings, data contracts, and optimal designs aligned to requirements.
Pipeline development: Build and test robust ETL/ELT for batch, micro-batch, and streaming across RDBMS, flat files, APIs, and event sources.
Performance and cost tuning: Profile and optimize jobs, right-size infrastructure, and model license/compute/storage costs.
Data modeling and storage: Design schemas and SCD strategies; manage relational, NoSQL, data lakes, Delta Lakes, and Lakehouse tables.
DevOps and release: Establish coding standards, templates, CI/CD, configuration management, and monitored release processes.
Quality and reliability: Define DQ rules and lineage; implement SLA tracking, failure detection, RCA, and proactive defect mitigation.
Security and governance: Enforce IAM best practices, retention, audit/compliance; implement PII detection and masking.
Orchestration: Schedule and govern pipelines with Airflow and serverless event-driven patterns.
Stakeholder collaboration: Clarify requirements, present design options, conduct demos, and finalize architectures with customer teams.
Leadership: Mentor engineers, set FAST goals, drive upskilling and certifications, and support module delivery and sprint planning.

REQUIRED QUALIFICATIONS:

Experience: 15+ years designing distributed systems at petabyte scale; 10+ years building data lakes and multi-source ingestion.
Cloud (AWS): IAM, VPC, EC2, EKS/ECS, S3, RDS, DMS, Lambda, CloudWatch, CloudFormation, CloudTrail.
Programming: Python (preferred), PySpark, SQL for analytics, window functions, and performance tuning.
ETL tools: AWS Glue, Informatica, Databricks, GCP DataProc; orchestration with Airflow.
Lakehouse/warehousing: Snowflake, BigQuery, Delta Lake/Lakehouse; schema design, partitioning, clustering, performance optimization.
DevOps/IaC: Terraform with 15+ years of practice; CI/CD (GitHub Actions, Jenkins) with 10+ years; config governance and release management.
Serverless and events: Design event-driven distributed systems on AWS.
NoSQL: 2–3 years with DocumentDB including data modeling and performance considerations.
AI services: AWS Entity Resolution, AWS Comprehend; run custom LLMs on Amazon SageMaker; use LLMs for PII classification.

NICE-TO-HAVE QUALIFICATIONS:

Data governance automation: 10+ years defining audit, compliance, retention standards and automating governance workflows.
Table and file formats: Apache Parquet; Apache Iceberg as analytical table format.
Advanced LLM workflows: RAG and agentic patterns over proprietary data; re-ranking with index/vector store results.
Multi-cloud exposure: Azure ADF/ADLS, GCP Dataflow/DataProc; FinOps practices for cross-cloud cost control.

OUTCOMES AND MEASURES:

Engineering excellence: Adherence to processes, standards, and SLAs; reduced defects and non-compliance; fewer recurring issues.
Efficiency: Faster run times and lower resource consumption with documented cost models and performance baselines.
Operational reliability: Faster detection, response, and resolution of failures; quick turnaround on production bugs; strong release success.
Data quality and security: High DQ pass rates, robust lineage, minimal security incidents, and audit readiness.
Team and customer impact: On-time milestones, clear communication, effective demos, improved satisfaction, and completed certifications/training.

LOCATION AND SCHEDULE:

● Location: Outside US (OUS).

● Schedule: Minimum 6 hours of overlap with US time zones.

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