NLB Services
Website:
nlbservices.com
Job details:
What We’re Looking For
• 15–20+ years of overall experience, with at least 15 years in solid, hands-on software
and data engineering roles before elevating into delivery leadership.
• Continued technical engagement in current role — architecture, design reviews, and
direct involvement in solving complex engineering problems.
• Proven track record of leading delivery organizations of 100–250 engineers across
multiple concurrent engagements.
• Strong problem-solving mindset and the executive presence to front-end senior client
conversations.
• Comfort operating across time zones and collaborating with global teams.
Technical Depth — Software Engineering
• Strong, hands-on background in modern application development — microservices,
event-driven architectures, API-first design, and domain-driven design.
• Deep proficiency across at least one major stack (Java/Spring, .NET, Python, Node.js)
and modern front-end frameworks (React, Angular, or equivalent).
• Cloud-native engineering on AWS, Azure, or GCP — containers, Kubernetes,
serverless, infrastructure-as-code (Terraform), and well-architected design principles.
• DevSecOps maturity — CI/CD pipelines, automated testing, shift-left security,
SAST/DAST, dependency scanning, and release engineering.
• Site Reliability Engineering practices — SLO/SLI definition, observability (logs, metrics,
traces), incident management, and chaos engineering.
• Modernization patterns — strangler fig, anti-corruption layers, monolith decomposition,
and re-platforming of legacy systems.
• Deep familiarity with AI-augmented software engineering — using GitHub Copilot,
Cursor, Claude Code, and similar tools to drive measurable productivity, quality, and
velocity improvements across SDLC.
• Working knowledge of agile delivery at scale — Scrum, SAFe, or equivalent — paired
with engineering metrics that actually matter (cycle time, change failure rate, defect
density).
Technical Depth — Data Engineering
• Strong, hands-on background in modern data engineering — batch and streaming
pipelines, ELT/ETL design, and large-scale data processing.
• Deep proficiency with data platforms such as Databricks, Snowflake, BigQuery, or
Redshift — including lakehouse architectures, medallion patterns, and data mesh
principles.
• Hands-on experience with Spark, Kafka/Kinesis, Airflow, dbt, and equivalent
orchestration and transformation tooling.
• Strong grasp of data modeling — dimensional modeling, Data Vault, and modern
schema-on-read patterns.
• Data quality, observability, and lineage — using tools such as Great Expectations,
Monte Carlo, OpenLineage, or equivalent.
• Data governance, security, and compliance — PII handling, access controls, masking,
residency, and alignment with enterprise data governance frameworks.
• Practical experience integrating data platforms with downstream analytics, ML, and AI
workloads — feature stores, vector stores, and AI-ready data products.
• Comfort with DataOps and MLOps practices — versioning, CI/CD for data, automated
testing of pipelines, and production monitoring.
• Demonstrated use of AI to accelerate data engineering — code generation for pipelines,
automated documentation, schema mapping, and test data generation.
Click on Apply to know more.