VP - Data Engineering

What are we looking for

real solver?

Solver? Absolutely. But not the usual kind. We're searching for the architects of the audacious . the pioneers of the possible. If you're the type to dismantle assumptions, re-engineer ‘best practices,’ and build solutions that make the future possible NOW, then you're speaking our language.

Your Responsibilities

what you will wake up to solve.

1. Global Delivery . Operational Rigor (The Process Setter)

• Unified Methodology:

Define and enforce a single, unified, globally standardized 'DataOps-First' methodology for all data engineering delivery (ETL/ELT pipelines, Data Modeling, MLOps, Data Governance). This mandate ensures predictable outcomes and trusted data integrity, eliminating architecture variability across SBUs.

• Operational Efficiency . Stewardship:

Drive strategic initiatives to optimize billable utilization and enhance operational efficiency across the practice. You are the steward of commercial success, ensuring all data delivery models (from migration to modern data stack implementation) are inherently profitable, scalable, and cost-effective.

• Execution Oversight:

Serve as the executive escalation point for critical delivery issues, personally intervening to resolve complex data integration bottlenecks and pipeline failures that threaten client timelines or data reliability standards.

• Quality Governance: Implement and audit technical data quality standards, ensuring all SBUs adhere to strict policies regarding data lineage, automated quality checks (observability), security/privacy compliance (GDPR/CCPA/PII), and proactive catalog management.

2. Strategic Growth . Practice Scaling (The Practice Architect)

• Talent . Scale Strategy:

Own the global strategy for data engineering talent acquisition, development, and retention. Implement objective metrics to assess and scale the 'Data-Native' DNA across the organization, ensuring we consistently staff high-impact data teams capable of handling petabyte-scale environments.

• Offerings Standardization:

Ensure that all regional offerings (e.g., Modern Data Platform, Data Mesh, Lakehouse Implementation) are built upon the standardized, profitable frameworks defined by the practice, accelerating time-to-insight and reducing architectural fragmentation.

• Innovation . IP: Drive the strategic integration of Vector Databases and LLM-ready architectures into all data practices and champion the development of IP and reusable accelerators (e.g., automated ingestion engines) that fundamentally improve delivery speed and data availability.

3. Leadership . SBU Management (The Executive Mentor)

• SBU Leadership Management:

Directly lead, mentor, and manage the Directors and Managers of all Data Engineering SBUs, holding them accountable for their regional operational consistency, talent development, and adherence to global data governance standards.

• Executive Communication:

Clearly articulate the data practice's operational status, quality metrics, and scaling strategy to the CTO, CDO, and other C-suite stakeholders.

• Ecosystem Partnership: Maintain executive-level relationships with key partners (Snowflake, Databricks, AWS/GCP) to align our delivery capabilities with their product roadmaps, securing joint training and enablement opportunities.

Welcome to Searce

The ‘process-first’, AI-native modern tech consultancy that's rewriting the rules.

We don’t do traditional.

As an engineering-led consultancy, we are dedicated to relentlessly improving the real business outcomes. Our solvers co-innovate with clients to
futurify operations and make processes smarter, faster . better.

Functional Skills

1. Executive Delivery . Operational Leadership

• Global Delivery Standardization:

Expert capability in defining, implementing, and auditing a unified, scalable delivery methodology (DataOps, Agile Data Warehousing, Mesh Principles) across geographically dispersed business units.

• Operational Efficiency Mastery:

Proven experience in managing and optimizing key operational metrics for a large data practice, including billable utilization, resource allocation, forecasting accuracy, and operational expense control.

• Contract . Risk Governance:

High proficiency in reviewing and managing complex Statement of Work (SOW) agreements for data initiatives, identifying and mitigating delivery risks (e.g., data availability, scope creep), and navigating commercial negotiations with clients and partners.

• Executive Escalation Management: Demonstrated ability to quickly and effectively manage the resolution of high-stakes, client-facing delivery failures or data integrity crises, restoring trust and defining clear remediation plans.

2. Technical and Architectural Governance (Hands-On Credibility)

• Cloud-Native Data Authority:

Deep, current technical knowledge of modern data stack design (Lakehouse, Data Mesh, MPP Warehousing) on hyperscalers (Snowflake, Databricks, GCP BigQuery, AWS Redshift). The ability to personally validate and course-correct complex architectural roadmaps is non-negotiable.

• Data Quality . Governance:

Expertise in establishing and auditing mandatory data quality standards, including automated observability (completeness, freshness, accuracy), regulatory compliance (GDPR/CCPA/PII), and proactive management of data lineage and cataloging across the entire portfolio.

• Advanced Domain Expertise:

Strong functional knowledge and experience leading solutions in high-growth areas like Generative AI (RAG pipelines, Vector Databases), Real-Time Streaming architectures, and large-scale platform migrations.

• DataOps . Orchestration: Expert knowledge of integrating and governing advanced CI/CD for data, orchestration frameworks (Airflow, Dagster), and Infrastructure as Code (IaC) practices across all delivery teams.

3. Practice Scaling . Commercial Stewardship

• SBU/BU Management:

Proven success in directly leading and mentoring Director/Manager-level leaders, holding them accountable for their operational metrics and talent development goals.

• Offerings Strategy . Scoping:

Expertise in designing, packaging, and pricing repeatable data service offerings (e.g., Data Maturity Assessments, Modern Data Stack implementations) to ensure competitive advantage and inherent profitability.

• Talent Strategy . Development:

Functional ability to design and implement standardized, objective growth frameworks for data careers (e.g., Analytics Engineer to Principal Data Architect) and scale high-performance data talent globally.

• Ecosystem Management: Functional competence in managing strategic, executive-level relationships with major Data . AI partners (Snowflake, Databricks, GCP/AWS) to drive co-sell motions and secure joint enablement resources.

Tech Superpowers

• Modern Data Architect

– Reimagines business with the Modern Data Stack (MDS) to deliver data mesh implementations, insights, . real value to clients.

• End-to-End Ecosystem Thinker

– Builds modular, reusable data products across ingestion, transformation (ETL/ELT), governance, and consumption layers.

• Distributed Compute Savant

– Crafts resilient, high-throughput architectures that survive petabyte-scale volume and data skew without breaking the bank.

• Governance . Integrity Guardian

– Embeds data quality, complete lineage, and privacy-by-design (GDPR/PII) into every table, view, and pipeline.

• AI-Ready Orchestrator

– Engineers pipelines that bridge structured data with Unstructured/Vector stores, powering RAG models and Generative AI workflows.

• Product-Minded Strategist

– Balances architectural purity with time-to-insight; treats every dataset as a measurable "Data Product" with clear ROI.

• Pragmatic Stack Curator

– Chooses the simplest tools that compound reliability; fluent in SQL, Python, Spark, dbt, and Cloud Warehouses.

• Builder @ Heart – Writes, reviews, and optimizes queries daily; proves architectures with cost-performance benchmarks, not slideware. Business-first, data-second, outcome focused technology leader.

Experience . Relevance

• Executive Experience:

Minimum 12. years of progressive experience in data engineering and analytics, with at least 5 years in a Senior Director or VP-level role managing multiple technical teams and owning significant operational and efficiency metrics for a large data service line.

• Delivery Standardization:

Demonstrated success in defining and implementing globally consistent, repeatable delivery methodologies (DataOps/Agile Data Warehousing) across diverse teams.

• Architectural Depth:

Must retain deep, current expertise in Modern Data Stack architectures (Lakehouse, MPP, Mesh) and maintain the ability to personally validate high-level architectural and data pipeline design decisions.

• Operational Leadership:

Proven expertise in managing and scaling large professional services organizations, demonstrated ability to optimize utilization, resource allocation, and operational expense.

• Domain Expertise:

Strong background in Enterprise Data Platforms, Applied AI/ML, Generative AI integration, or large-scale Cloud Data Migration.

• Communication: Exceptional executive-level presentation and negotiation skills, particularly in communicating complex operational, data quality, and governance metrics to C-level stakeholders.

Join the ‘real solvers’

ready to futurify?

If you are excited by the possibilities of what an AI-native engineering-led, modern tech consultancy can do to futurify businesses, apply here and experience the ‘
Art of the possible
’.
**Don’t Just Send a Resume. Send a Statement.