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Technical Data Lead
Technical Data LeadBetis Group, Inc. • New York, NY, US
Technical Data Lead

Technical Data Lead

Betis Group, Inc. • New York, NY, US
29 days ago
Job type
  • Full-time
Job description

Job Description

Job Description

About the Role :

The Technical Data Lead will play a pivotal role in driving the strategic direction and execution of data initiatives within the organization. This position is responsible for overseeing the design, development, and implementation of data solutions that support business objectives and enhance data-driven decision-making. The role requires collaboration with cross-functional teams to ensure data integrity, security, and accessibility while managing a team of data professionals. The Technical Data Lead will also establish best practices and governance frameworks to optimize data quality and compliance. Ultimately, this role ensures that data assets are leveraged effectively to deliver actionable insights and competitive advantage. You will be The Technical authority for data analysis / profiling, mapping / lineage, cleansing automation, conversion scripting, performance tuning, reconciliation, audit evidence, and automated comparison reporting for the legacy→PAS data migration

Minimum Qualifications :

  • Relevant Oracle data‑migration certification (or closely related).
  • 7+ years hands‑on Oracle (19c preferred) : SQL / PL‑SQL, ETL / ELT, bulk‑load utilities, and performance optimization.
  • Proven experience scripting large‑scale conversions and delivering reconciliation / balancing automation and comparison reports.
  • Documented contribution to Data Conversion Plans and production run books with formal sign‑offs.
  • Strong PII / security discipline and experience working in public‑sector environments with IV&V.

Preferred Qualifications :

  • Vitech V3 / V3Locity experience in pension / benefits administration.
  • Experience with NYC payroll data sources and Microsoft Access‑based source systems.
  • Prior success in rollout‑based conversion programs including parallel / reverse‑parallel processing (“bridging”).
  • Bachelor’s degree in Computer Science, Information Technology, Data Science, or a related field or equivalent experience.
  • Responsibilities :

  • Reverse‑engineer legacy schema and code; produce detailed data mappings to PAS; identify missing elements; contribute to the Final Data Analysis Plan.
  • Design and evolve staging models and cleansing pipelines for outside sources; implement reconciliation and balancing between legacy, staging, and PAS; produce automated comparison reports and auditable logs.
  • Script and tune conversion runs (order of operations, runtime expectations, risk points); execute test cycles; correct defects; prepare for and execute final production conversion.
  • Implement and support mini‑conversions / “bridging” during parallel and reverse‑parallel processing; provide post‑production data support as needed.
  • Co‑author the Data Conversion Plan with the Data Manager; define roles / responsibilities, scripts, reconciliation tools, and sign‑off procedures; develop performance test scripts and tuning profiles.
  • Participate in PAS design sessions to inform data constraints, transformation rules, and system limitations.
  • Provide technical inputs for weekly / bi‑weekly status and Jira updates; maintain clear, repeatable technical documentation suitable for audit.
  • Skills :

    The Technical Data Lead utilizes strong analytical and problem-solving skills daily to design and optimize data systems that meet business needs. Proficiency in SQL and data warehousing enables the candidate to build efficient data pipelines and ensure seamless data integration. Leadership and communication skills are essential for managing a diverse team and collaborating with stakeholders to align technical solutions with strategic goals. Familiarity with cloud platforms and programming languages enhances the ability to implement scalable and flexible data architectures. Additionally, knowledge of data governance and compliance ensures that data practices adhere to legal and ethical standards, maintaining organizational trust and data security.

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