Practice Head-Software engineering

1520+ 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 100250 engineers across

multiple concurrent engagements.

Industry expertise in at least one of Innovers focus verticals Manufacturing, Logistics,

Technology, or Telecom (BFS not required).

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,