Consultant - Data Engineer
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Before you apply
LocationHyderabad, India
Typefull-time
SeniorityLead
Experience8–12 yrs
DepartmentEngineering & Technology
Company size10,000+ people
First seenOct 30, 2026 · 6d ago
Verified live2d ago
At a glanceSummarised by Seekless from the posting.
8 to 12 years of experience
12+ years of data engineering experience
5+ years in technical leadership, architecture, or lead engineer responsibilities
Bachelor's (Engineering)/Master's in computer science or a related field
Deep expertise in Snowflake
Deep expertise in AWS
Deep expertise in Python
Deep expertise in SQL
Deep expertise in Airflow
Deep expertise in GitHub
Deep expertise in relational data platforms
Strong capability in data architecture
Strong capability in dimensional modeling
Strong capability in pipeline optimization
Strong capability in data quality
Strong capability in governance engineering
Proven experience designing reusable frameworks
Proven experience designing automation patterns
Proven experience designing CI/CD practices
Proven experience designing production-ready data products
Leadership in architecture reviews
Leadership in technical strategy
Leadership in discovery
Leadership in dependency management
Leadership in delivery-risk mitigation
Proven outcome driven usage of AI-powered engineering tools, including GitHub Copilot, Snowflake Cortex, and other approved GenAI solutions
Strong communication skills
Mentoring skills
Stakeholder influence
Cross-team collaboration skills
Snowflake dimensional modeling, performance tuning, workload optimization, and data quality controls
Hands-on experience with Snowpark for building scalable data transformation frameworks
Change data capture, secure data sharing, masking, backfill, and reprocessing patterns
Snowflake error handling, pipeline resilience, and integration with AWS services such as S3, Glue, and Lambda
Advanced SQL tuning, query profiling, clustering, partitioning, and workload management for large analytical workloads
AWS data engineering using S3, Glue, Lambda, IAM, CloudWatch, Secrets Manager, Step Functions, and EventBridge
Secure, scalable, cost-aware cloud data processing using batch, event-driven, and serverless patterns
Cloud security, encryption, access policies, observability, and operational resilience
Python and Snowpark for data processing, automation, APIs, reusable frameworks, testing utilities, and operational scripts
Advanced SQL for complex transformations, reconciliation, performance tuning, and analytical validation
Reusable libraries, templates, and automation patterns that improve engineering productivity
Airflow or equivalent orchestration for dependency management, scheduling, monitoring, retries, and recovery
CI/CD, automated deployments, environment promotion, release governance, and rollback planning
Logging, monitoring, alerting, service-level indicators, and production readiness for critical data products
Data modeling, canonical structures, metadata-driven design, lineage, data contracts, and governed data products
Data quality rules, reconciliation frameworks, audit controls, privacy classifications, and secure access patterns
Architecture trade-off analysis across performance, scalability, maintainability, cost, security, and business value
Data reconciliation, SQL/Python automated testing, regression testing, and validation strategy
UAT coordination, defect triage, and quality-risk escalation
GitHub branching, pull request standards, CI checks, and quality gates
Oracle Database — PL/SQL development, performance tuning, data extraction
Informatica PowerCenter — ETL workflows, mappings, reverse-engineering legacy jobs
Enterprise Schedulers — TWS, Control-M, Autosys for dependency mapping
Claims lifecycle mapping, TAT/SLA metric definitions, and NIGO process understanding
Payments and recoveries analytics, including Worksite versus Group Disability nuances
PII classification, access controls, least privilege, auditability, and lineage
Business glossary, definitions, controls documentation, and evidence
Cloud certifications in AWS or Snowflake, such as SnowPro Core or Advanced, are preferred
Contributions to communities of practice, domain academies, engineering forums, product mindset conversations, or reusable knowledge repositories are valued
Skills
Snowflake
AWS
Python
SQL
Airflow
GitHub
Snowpark
S3
Glue
Lambda
IAM
CloudWatch
Secrets Manager
Step Functions
EventBridge
Oracle
PL/SQL
Informatica PowerCenter
Control-M
Autosys
TWS
GitHub Copilot
Snowflake Cortex
What You’ll Do
What You’ll do
About the role
As a Consultant Data Engineer at Principal Financial Group, you will lead enterprise-scale data engineering delivery for our Data & Analytics Technology Program. This role will define technical direction, guide architecture decisions, mentor engineers, improve engineering culture, and drive predictable, high-quality delivery. The ideal candidate brings deep hands-on expertise in Snowflake, AWS, Python, SQL, and modern orchestration, with the ability to translate business and product priorities into resilient, governed, and scalable data products.
You’ll have opportunity to:
•
Own technical direction for complex, enterprise-scale data engineering initiatives across Snowflake, AWS, and orchestration platforms.
•
Lead architecture, design, and operational readiness for resilient, secure, scalable, and governed data products.
•
Partner with Product Managers, Architects, SRE, and Engineering Leads to translate business priorities into feasible technical roadmaps.
•
Guide discovery and solution-shaping by assessing feasibility, platform capability, architecture fit, data dependencies, and delivery risks.
•
Drive engineering excellence through quality practices, delivery metrics, DevOps adoption, secure coding, automated testing, and continuous improvement.
•
Mentor engineers, raise technical standards, and build reusable patterns, frameworks, and knowledge assets for the team.
•
Communicate progress, risks, trade-offs, and decisions clearly to senior stakeholders.
Who You Are
Who You are:
Experience: 8 to 12 years
Education:
Bachelor’s(Engineering)/Master’s in computer science or a related field.
•
12+ years of data engineering experience, including significant hands-on delivery and 5+ years in technical leadership, architecture, or lead engineer responsibilities.
•
Deep expertise in Snowflake, AWS, Python, SQL, Airflow, GitHub, and relational data platforms.
•
Strong capability in data architecture, dimensional modeling, pipeline optimization, data quality, and governance engineering.
•
Proven experience designing reusable frameworks, automation patterns, CI/CD practices, and production-ready data products.
•
Leadership in architecture reviews, technical strategy, discovery, dependency management, and delivery-risk mitigation.
•
Proven outcome driven usage of AI-powered engineering tools, including GitHub Copilot, Snowflake Cortex, and other approved GenAI solutions, to improve developer productivity, code quality, documentation, test case generation, and troubleshooting, while ensuring compliance with organizational security, privacy, and responsible AI guidelines.
•
Strong communication, mentoring, stakeholder influence, and cross-team collaboration skills.
Must-Have Skills
Data Engineering — Snowflake
•
Snowflake dimensional modeling, performance tuning, workload optimization, and data quality controls.
•
Hands-on experience with Snowpark for building scalable data transformation frameworks.
•
Change data capture, secure data sharing, masking, backfill, and reprocessing patterns.
•
Snowflake error handling, pipeline resilience, and integration with AWS services such as S3, Glue, and Lambda.
•
Advanced SQL tuning, query profiling, clustering, partitioning, and workload management for large analytical workloads.
Cloud & Platform Engineering — AWS
•
AWS data engineering using S3, Glue, Lambda, IAM, CloudWatch, Secrets Manager, Step Functions, and EventBridge.
•
Secure, scalable, cost-aware cloud data processing using batch, event-driven, and serverless patterns.
•
Cloud security, encryption, access policies, observability, and operational resilience.
Programming & Automation — Python / SQL
•
Python and Snowpark for data processing, automation, APIs, reusable frameworks, testing utilities, and operational scripts.
•
Advanced SQL for complex transformations, reconciliation, performance tuning, and analytical validation.
•
Reusable libraries, templates, and automation patterns that improve engineering productivity.
Orchestration, DevOps & Observability
•
Airflow or equivalent orchestration for dependency management, scheduling, monitoring, retries, and recovery.
•
CI/CD, automated deployments, environment promotion, release governance, and rollback planning.
•
Logging, monitoring, alerting, service-level indicators, and production readiness for critical data products.
Data Architecture & Governance Engineering
•
Data modeling, canonical structures, metadata-driven design, lineage, data contracts, and governed data products.
•
Data quality rules, reconciliation frameworks, audit controls, privacy classifications, and secure access patterns.
•
Architecture trade-off analysis across performance, scalability, maintainability, cost, security, and business value.
QA / Testing — Data
•
Data reconciliation, SQL/Python automated testing, regression testing, and validation strategy.
•
UAT coordination, defect triage, and quality-risk escalation.
•
GitHub branching, pull request standards, CI checks, and quality gates.
Good-to-Have Skills
Data Engineering
•
Oracle Database — PL/SQL development, performance tuning, data extraction
•
Informatica PowerCenter — ETL workflows, mappings, reverse-engineering legacy jobs
•
Enterprise Schedulers — TWS, Control-M, Autosys for dependency mapping
Claims Domain — Insurance
•
Claims lifecycle mapping, TAT/SLA metric definitions, and NIGO process understanding.
•
Payments and recoveries analytics, including Worksite versus Group Disability nuances.
Data Governance & Controls
•
PII classification, access controls, least privilege, auditability, and lineage.
•
Business glossary, definitions, controls documentation, and evidence.
Cloud certifications in AWS or Snowflake, such as SnowPro Core or Advanced, are preferred. Contributions to communities of practice, domain academies, engineering forums, product mindset conversations, or reusable knowledge repositories are valued.
Additional Information
Our Engineering Culture:
In our Agile/Lean DevOps environment, we’ve nurtured a culture of innovation and experimentation across our development teams. As a customer-focused organization, we collaborate closely with our end users and product owners to understand and rapidly respond to emerging business needs. Collaboration is ingrained into every aspect of our work – from the products we develop to the world-class service we offer. We are motivated by the belief that diversity of thought, background, and perspective is crucial to crafting the finest products and experiences for our customers. Come join us and become a part of a highly ambitious team dedicated to delivering impeccable solutions!