Senior Director - Data Engineering
First seen by Alion on Oct 29, 2026.
Role : Sr. Director / AVP Data Engineering
Location : All Straive Locations (Pune, Mumbai, Chennai, Hyderabad, Bengaluru, Gurugram, Noida)
About Us :
Straive is a global leader in data analytics and AI operationalization, helping enterprises embed advanced AI and data capabilities into core business workflows to deliver measurable business outcomes and ROI. With a workforce of ~20,000 professionals serving 350+ clients across 30+ markets, Straive combines technical scale with deep domain expertise. A key differentiator is its network of over 6,000 subject matter experts who specialize in managing and enriching complex, unstructured data enabling organizations to build AI systems grounded in accuracy, context, and business relevance.
The company continues to earn recognition from leading industry analysts and was recently named a Leader in AIM's 2026 Generative AI and Data Engineering PeMa Quadrants. Backed by EQT - a purpose-driven global investment organization which was ranked among the world's leading private equity firms by PEI in 2025 - Straive is positioned as a high-value alternative to traditional IT services providers, combining domain-led intelligence with execution at scale.
About the Role
Straive is seeking a hands-on technology leader to serve as the senior technical authority for our enterprise Data Engineering, Data Modernization, and Cloud Analytics Solutions.
In this AVP-equivalent executive role, you will set technical direction, resolve complex data engineering challenges, and move enterprise data architectures from initial blueprint to resilient, high-throughput, and secure production environments. The role combines deep data platform architecture with execution leadership, including a mandate to optimize data economics, pipeline latency, compute efficiency, and multi-cloud infrastructure ROI.
This is not a conventional delivery-governance role. You will remain close to the code and data architecture, lead critical design and PySpark/SQL/ETL code reviews, mentor senior architects, and take direct accountability for high-stakes, petabyte-scale technical outcomes across our global client footprint.
Key Responsibilities
1. Enterprise Data Platform Architecture & Production Engineering:
- Own Data Engineering Standards: Across architecture, pipeline orchestration, data modeling, testing, deployment, security, data observability, and lineage.
- Ensure Production-Grade Reliability: Introduce architecture design reviews, PySpark/SQL code reviews, automated testing (e.g., dbt test, Great Expectations), quality gates, and release-readiness protocols across all client deliveries.
- Resolve Technical Bottlenecks: Close critical gaps in data pipeline SLA/SLO adherence, data freshness, idempotency, throughput, schema evolution, fault tolerance, and disaster recovery from design through continuous operation.
2. Data Platform Economics, FinOps & Performance Optimization:
- Architect Modern Data Lakehouses: Based on throughput, query performance, storage tiering, data freshness, latency, and compliance requirements across AWS, Azure, and GCP.
- Optimize Operating Economics: Implement FinOps governance, cluster auto-scaling/right-sizing, query tuning, partition/z-ordering optimization, and storage lifecycle policies across Databricks, Snowflake, BigQuery, and Synapse.
- Drive Real-Time & Batch Pipeline Excellence: Engineered with strict zero-trust security, RBAC/ABAC governance, and end-to-end data encryption (at rest and in transit).
3. Technical Governance, DataOps & Reusable Assets:
- Serve as Final Escalation Point: For high-concurrency data platforms, pipeline failures, schema drifts, and complex multi-cloud cloud integrations.
- Modernize Enterprise Platforms: Guide microservices, REST/GraphQL APIs, semantic layers, event-driven streaming (Kafka/Flink), and enterprise catalog integrations (Collibra, Purview, Alation, Unity Catalog).
- Promote Engineering Reusability: Establish reusable ETL/ELT frameworks, automated ingestion harnesses, Infrastructure-as-Code (Terraform), DataOps CI/CD pipelines, and data quality accelerators that boost delivery speed.
4. Team Capability & Engineering Culture:
- Mentor Senior Technical Talent: Lead Engineers, and Data Developers through hands-on architecture reviews, code reviews, and deep-tech workshops.
- Build Autonomous Capability: Identify critical skill gaps in emerging data technologies (Delta Lake, Iceberg, Hudi, dbt, Dagster) and drive targeted technical hiring and upskilling.
- Raise Engineering Standards: Foster a culture of engineering ownership, technical rigor, experimentation, data reliability engineering (DRE), and continuous improvement.
5. Client Solutioning, Pre-Sales & Executive Advisory:
- Drive Commercial Solutioning: Partner with commercial, consulting, and practice leaders on client discovery workshops, technical proposals, RFPs, technical due diligence, and enterprise data strategy blueprints.
- Translate Business Problems: Deconstruct complex business requirements into pragmatic, cost-effective data architecture roadmaps, technical effort estimations, and ROI frameworks.
- Executive Stakeholder Alignment: Represent Straive as a trusted advisor to client CIOs, CTOs, and Chief Data Officers (CDOs).
Required Experience and Qualifications:
- 12 - 18 years of professional software and data engineering experience, with a minimum of 8+ years in dedicated data platform architecture and execution leadership within a top-tier analytics consulting firm, product company, or high-growth enterprise environment.
- Demonstrated track record of architecting, building, and deploying petabyte-scale batch and real-time streaming data platforms from concept to sustained, high-concurrency production adoption.
- Deep hands-on engineering capability in Python, PySpark, SQL, and Scala or Java, with a proven ability to write and review production-grade data pipeline code.
- Experience in Data Analytics is strictly non-negotiable. Must possess deep expertise bridging core data infrastructure with downstream BI, predictive analytics, feature stores, and enterprise reporting ecosystems.
- Extensive experience with Databricks (Delta Lake), Snowflake, AWS (EMR, Glue, Redshift), Azure (Synapse, Data Factory, Databricks), GCP (BigQuery, Dataflow), or dbt.
- Proven expertise in designing enterprise Data Lakehouses, Data Mesh, Data Fabric, Data Vault 2.0, and dimensional modeling (Kimball/Inmon).
- Hands-on experience with pipeline orchestration (Apache Airflow, Prefect, Dagster) and streaming technologies (Apache Kafka, Confluent, Spark Streaming, Flink).
- Strong command of DataOps, Data Quality frameworks (Great Expectations, dbt tests), Data Observability (Monte Carlo, Acceldata), CI/CD pipelines (GitHub Actions, Azure DevOps), and IaC (Terraform).
- Demonstrated ability to drive FinOps, drastically reducing cloud compute/storage costs while maintaining or improving pipeline throughput and SLA metrics.
- Proven track record of mentoring senior technical professionals (Principal Architects, Staff Engineers) and presenting complex architectural decisions to C-suite client executives.
Preferred Experience:
- Experience building multi-tenant SaaS data platforms, reusable enterprise data ingestion frameworks, or regulated/security-sensitive data platforms (HIPAA, GDPR, SOC2).
- Active participation in technical pre-sales, client RFPs, solution estimation, and competitive positioning against traditional global IT integrators.
- Hands-on exposure to feature engineering pipelines, MLOps integrations, or vector database integration for GenAI analytics workloads.
Leadership Profile:
- Hands-On & Accountable: Stays close to code, schemas, and pipeline performance; takes direct ownership of critical technical roadblocks and drives resolution.
- Commercially Pragmatic: Balances cutting-edge data architecture ambitions with pragmatic client value, delivery velocity, maintainability, and total cost of ownership (TCO).
- Constructive Challenger: Identifies pipeline flaws and architectural debt early, challenges complacent assumptions, and relentlessly raises engineering benchmarks.
- Capability Builder: Develops strong, independent technical leads and architects rather than becoming a single point of technical bottleneck.
Recognition & Achievements:
- We have been recognized as a Star Performer in Data & AI Services Specialists - Everest's North America PEAK Matrix 2025, and as a Leader in AIM's PeMa Quadrant of Agentic AI Service Providers - 2025.
- In Nov 2023, Straive acquired Gramener, an award-winning, design-led data science company, enhancing our data, analytics, and AI capabilities. In June 2025, we acquired SG Analytics, a leading provider of AI-powered insights and contextual analytics services.
For more information, please visit our Website: https://www.straive.com/
Equal Opportunity Employer:
Straive is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All employment decisions are based on business needs, job requirements, and individual qualifications, without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran, or disability status.
Skills
IT Head, Data Engineering, Cloud, Analytics, Data Governance, Python, PySpark, SQL, Databricks, Data Pipeline, Data Quality