Lead Data Engineer

About The Ksquare Group

The Ksquare Group is a global technology consulting company headquartered in Dallas, Texas, with over 14 years of experience delivering technology solutions across Salesforce, cloud, data, AI, and network engineering for clients in Banking & Financial Services, Public Sector, Telecommunications, Manufacturing, Logistics, Healthcare, and Enterprise Services. With a team of 350+ professionals across the US, Mexico, the Dominican Republic and India, we help organizations solve complex business challenges and achieve measurable outcomes through our collaborative POD-based delivery model. At The Ksquare Group, we foster a culture of integrity, collaboration, continuous learning, and professional growth, empowering our teams to make a meaningful impact for clients worldwide.

About the Role

We are seeking a **Tech Lead – Data Engineering** to own the technical delivery of Data Cleanup & establishing Single Source of Truth datalake along with data quality and trust. The resource will work closely with data architects, DataOps engineers, reporting teams, application teams, and business stakeholders to build scalable, secure, and high-performing data pipelines and analytical data models. This role requires someone equally comfortable running a technical de-duplication/cleansing pipeline.

Key Responsibilities

• Lead full AI-assisted profiling of the data to precisely size duplicates, placeholder entries, and stale/conflicting records ahead of cleansing.
• Design and execute **probabilistic de-duplication** and name/address standardization across legacy and data.
• Implement unique-citizen-identifier anchoring across every record so all benefit programs that operate from one trusted registry.
• Run each cleansing wave through structured verification with data stewards before promotion to production — this is an adjudication process, not a one-way automated pass.
• Stand up the **ongoing data governance framework** that protects the cleansed registry going forward: stewardship roles, validation-at-entry rules, controlled reference data, change control, and data-quality KPIs.
• Define and instrument data quality KPIs/SLAs (completeness, duplication rate, freshness, identifier-match confidence) with dashboards visible to program governance and leadership.
• Ensure the API and underlying data model support conflict-safe synchronization for the offline-first intake application (store-and-forward sync from low-connectivity regions).
• Ensure DocuSign-based digital signature workflows have clean, correctly identified citizen/case records to bind to.
• Establish data contracts and schema/versioning discipline between data, the API layer, the citizen portal, the offline app, and downstream ERP modules.
• Ensure PII handling, access control, and audit-trail requirements appropriate to a government beneficiary registry are built into the platform from the start.
• Feed the Monitoring & Evaluation (M&E) framework with adoption and data-quality metrics for program governance and executive reporting.
• Set engineering standards, review technical designs, and lead a small team of data engineers/analysts through an aggressive, AI-accelerated timeline.
• Act as the primary technical point of contact for the Deep-Dive Discovery: validating current state, running a data-profiling preview to size the cleansing effort, and locking down dependencies.
• Escalate and manage risk around known dependencies — schedule slippage, SME availability so proactive tracking and communication is essential.
• Support UAT, training, and hyper care for data-quality-related workflows; ensure data stewards are equipped to sustain governance rules post-go-live.
• Mentor Junior and mid-level data engineers

Required Qualifications

• 8+ years in data engineering, with 3+ years leading technical teams on large-scale data cleansing, migration, or MDM (Master Data Management) programs
• Demonstrated, hands-on experience with large-scale data de-duplication and entity resolution (probabilistic/fuzzy matching, standardization of names/addresses, unique-identifier anchoring) — this is the core deliverable of the role, not a peripheral skill.
• Proven experience designing data governance frameworks: stewardship models, validation-at-entry rules, quality KPIs, and change control that prevent data quality from regressing after a cleanup.
• Strong API design and development experience (RESTful/secure API layers over a system of record, versioning, documentation) suitable for multiple consumer channels (web portal, offline/mobile, future integrations).
• Experience with offline-first / low-connectivity data synchronization patterns (conflict resolution, store-and-forward sync).
• Comfort working directly with non-technical business/data stewards to adjudicate conflicting records — this role requires facilitation skill as much as engineering skill.
• Strong SQL and at least one programming language used in data engineering (Python preferred, given AI-assisted profiling/matching tooling).
• Understanding of PII/data protection handling appropriate to citizen/beneficiary data in a government context.
• Excellent stakeholder communication skills — ability to present cleansing progress, risk, and data quality metrics to leadership and programme governance.