SysTechCorp Inc
Website:
systechus.com
Job details:
Company Description SysTechCorp Inc delivers leading-edge technology services that help clients achieve and surpass their strategic business objectives. The company’s practices in AI, machine learning, mobile, cloud, and ERP are supported by years of hands-on experience and deep domain expertise. SysTechCorp works across a wide range of industry verticals, offering tailored solutions that address complex business and data challenges. Team members collaborate with clients to modernize systems, leverage advanced analytics, and create scalable, future-ready technology platforms.
Role Description This is a full-time remote role for a Data Modeler at SysTechCorp Inc. The Data Modeler will design, develop, and maintain logical and physical data models to support analytics, applications, and enterprise systems. Day-to-day responsibilities include analyzing business requirements, defining data structures and relationships, documenting data dictionaries, and ensuring data models align with performance, security, and compliance standards. The role involves collaborating with data engineers, architects, and business stakeholders, reviewing existing data architectures, optimizing data flows, and supporting integration projects across AI, ML, cloud, and ERP platforms. The Data Modeler will also troubleshoot data-related issues, perform impact analysis for changes, and contribute to best practices and standards for data governance and metadata management.
Position: Data Modeler
Location: Hyderabad/Remote
Availability: Immediate joiner
What You’ll Do
In this role you will:
- Analyze customer data estates across Financial Services, Healthcare, Telecommunications, and other regulated verticals, and produce authoritative source-to-target mappings from customer physical schemas onto Industry Data Models (IDMs) — covering entities, attributes, keys, grain definitions, and business logic transformations.
- Define and maintain IDM mapping standards, canonical equivalence patterns, and naming conventions for each supported industry vertical — the governing artefacts that AI mapping agents use at runtime to propose and validate mappings autonomously.
- Define and provide behavioral ground truth for the AI data modelling agent: annotate correct mappings, flag incorrect proposals, and document the reasoning behind every accepted or rejected agent decision — forming the authoritative reference the agent is trained and evaluated against.
- Build and curate high-quality golden datasets for agent training and evaluation — multi-industry, multi-domain mapping examples spanning clean cases, edge cases, ambiguous entities, cross-system synonyms, and known failure modes.
- Design and generate synthetic data sets that faithfully reproduce the structural and statistical properties of real customer schemas without exposing customer data — enabling safe, scalable agent training, regression testing, and evaluation suite expansion.
- Validate AI-generated outputs: review auto-proposed data models, source-to-target mappings, and dbt/SQL transformation artefacts for correctness, completeness, and adherence to IDM and governance standards.
- Define, author, and govern the Semantic Data Type vocabulary — connecting physical columns to governed business concepts and data quality expectations across all supported IDM verticals.
- Author and maintain the Business Glossary for each industry domain: terms, definitions, synonyms, hierarchies, and relationships; drive import of industry-standard glossaries (FIBO for Financial Services, FHIR/OMOP for Healthcare, TM Forum for Telecommunications, CDMC cross-vertical).
- Review and curate semantic type assignments produced by the platform's automated tagging and classification pipeline; act as the authoritative steward for the controlled Semantic Data Type vocabulary.
- Collaborate with Graph Engineers on Context Graph schema design — ensuring the graph model encodes IDM entity relationships, equivalence groups, and cross-industry concept alignments in a form traversable by AI agents at inference time.
- Work with Data Quality engineers to ensure IDM-aligned Semantic Data Types are correctly linked to DQ expectations, validation rule sets, and SLA categories for each vertical.
- Produce and maintain enterprise data modelling guiding principles and naming standards consumed by AI agents at runtime to generate consistent, governed, industry-aligned artefacts.
Who You’ll Work With
On our team, we:
- Are part of Teradata’s global engineering organization, responsible for building the technologies that power VantageCloud, our unified data and AI platform.
- Operate at the intersection of cloud computing, advanced analytics, and AI-driven automation to help enterprises unify and analyze data across hybrid and multi-cloud environments.
- Solve highly complex challenges in scalability, performance, interoperability, and intelligent automation to enable customers to turn data into insights and innovation.
- Collaborate across research, architecture, platform engineering, and product teams to shape the future of enterprise AI.
- This position reports into the AI engineering leadership team within Teradata’s global engineering organization.
What Makes You a Qualified Candidate
- Proven experience in data modelling: conceptual, logical, and physical model design across relational and dimensional paradigms.
- Hands-on experience with one or more industry canonical data models: FIBO (Financial Services), FHIR or OMOP (Healthcare), TM Forum (Telecommunications), CDMC, or equivalent.
- Experience mapping customer physical schemas onto canonical or reference data models — including multi-hop source-to-target mappings, grain alignment, and business logic documentation.
- Ability to validate AI/LLM-generated data modelling outputs and articulate precisely why a proposed mapping is correct, incorrect, or ambiguous.
- Experience building evaluation or golden datasets: sampling strategies, edge case coverage, annotation workflows, and inter-annotator agreement.
- Experience with synthetic data generation techniques for structured/tabular data.
- Strong SQL proficiency — able to trace column-level lineage through multi-hop transformations, stored procedures, and views.
- Experience with data governance frameworks: data stewardship, DQ rule design, metadata lifecycle management.
What You’ll Bring
- Bachelor's or Master's degree in Computer Science, Information Systems, or a related field.
- 4–7+ years of experience in data modelling, data architecture, or enterprise data management.
- Deep expertise in at least one industry reference model framework (FIBO, FHIR, OMOP, TM Forum SID, CDMC, or equivalent) — with the ability to apply it to real customer schemas.
- Hands-on experience producing source-to-target mapping specifications consumed by transformation tools (dbt, ETL, or AI agents).
- Experience authoring and governing business glossaries or ontologies across industry domains — in formal tools or platform-native environments.
- Experience designing golden datasets and evaluation corpora for AI or rules-based mapping systems; familiarity with annotation tooling and evaluation metric design.
- Experience with synthetic data generation for structured schemas — statistical fidelity, referential integrity preservation, and privacy-safe techniques.
- Advanced SQL skills: complex analytical queries, column-level lineage tracing through multi-hop transformations, stored procedures, and UDFs.
- Familiarity with dbt for transformation modelling and dataset management.
- Strong written communication skills — able to document mapping decisions and modelling rationale for both technical engineers and business domain stakeholders.
- Familiarity with graph data models or semantic web standards (RDF, OWL, SKOS) is a plus
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