About Infinitive:
Infinitive is a data and AI consultancy that enables its clients to modernize, monetize and operationalize their data to create lasting and substantial value. . We possess deep industry and technology expertise to drive and sustain adoption of new capabilities. We match our people and personalities to our clients' culture while bringing the right mix of talent and skills to enable high return on investment.
Infinitive has been named "Best Small Firms to Work For" by Consulting Magazine 9 times most recently in 2026. Infinitive has also been named a Washington Post "Top Workplace", Washington Business Journal "Best Places to Work", and Virginia Business "Best Places to Work."
Role Overview
We are seeking a highly technical Ab Initio Subject Matter Expert who can do more than develop or maintain graphs. This role requires someone who deeply understands how Ab Initio works, including GDE graphs, DML, metadata, EME, Control Center, reusable components, and batch execution patterns, and can reverse-engineer what existing Ab Initio applications are actually doing.
The ideal candidate is a technical translator: someone who can analyze complex Ab Initio graphs, interpret the embedded business logic, explain the flow of data, and document the intent clearly enough for another engineering team to recreate, modernize, or migrate the functionality.
This role is especially important in environments where Ab Initio has become a legacy "black box" and the organization needs to understand, document, and potentially rebuild those processes on a modern data platform.
Key Responsibilities
Ab Initio Graph Analysis & Reverse Engineering
Analyze existing Ab Initio applications, graphs, plans, DML, XFRs, parameter sets, sandbox structures, and metadata to understand how data is sourced, transformed, validated, enriched, and delivered.
Deconstruct complex graph logic and identify:
Source and target datasets
Joins, lookups, filters, rollups, scans, partitions, sorts, and reformat logic
Data quality rules and exception handling
Business rules embedded in transformations
Dependencies across graphs, components, schedules, and downstream consumers
Restart, recovery, and operational control logic
Technical Translation & Documentation
Translate Ab Initio logic into clear technical documentation that can be used by data engineers, architects, migration teams, QA teams, and business stakeholders.
Create detailed artifacts such as:
Source-to-target mappings
DML and metadata interpretation summaries
Graph logic walkthroughs
Transformation rule documentation
Dependency and lineage maps
Functional specifications for graph recreation or migration
Test scenarios and reconciliation rules
The goal is not simply to document what the graph does technically, but to explain why it does it and how to preserve the same business outcome in a new implementation.
Business Logic Interpretation
Partner with business, data, risk, finance, operations, and technology stakeholders to understand the business purpose behind existing Ab Initio processes.
Be able to explain complex data flows in plain language, including how a graph supports reporting, reconciliation, customer processing, regulatory needs, risk controls, financial calculations, or operational workflows.
Metadata, Lineage & Dependency Discovery
Use Ab Initio EME, Metadata Hub, Control Center, project repositories, scripts, schedules, logs, and related documentation to identify upstream and downstream impacts.
Support end-to-end lineage discovery across:
Source systems
Intermediate files
Serial and multifile datasets
DML records
Lookup files
Database tables
Output feeds
Downstream applications and reports
Migration & Modernization Support
Support initiatives to recreate or migrate Ab Initio logic to modern platforms such as Databricks, Spark, Snowflake, cloud-native data pipelines, or other enterprise data platforms.
Work closely with migration teams to ensure the new implementation preserves the intent, business rules, control logic, and data outcomes of the original Ab Initio process.
Help determine where to modernize, simplify, optimize, or redesign versus where to perform a like-for-like translation.
Performance & Operational Understanding
Analyze graph design, execution patterns, partitioning strategies, data volumes, component usage, and runtime behavior to identify performance bottlenecks or operational risks.
Interpret logs, job failures, restart logic, dependencies, Control Center executions, and environmental scripts to understand how processes run in production.
Mentorship & Standards
Provide guidance to development, migration, and QA teams on Ab Initio best practices, graph interpretation, reusable components, metadata management, testing, and documentation standards.
Help establish repeatable patterns for Ab Initio assessment, documentation, reverse engineering, and migration readiness.
Required Experience & Skills
Core Ab Initio Expertise
7+ years of hands-on Ab Initio experience in complex enterprise environments
Deep knowledge of GDE, Co>Operating System, EME, Control Center, DML, XFR, plans, sandboxes, multifile systems, and reusable components
Ability to read, interpret, and explain complex Ab Initio graphs without relying only on original developers
Strong understanding of batch processing, parallelism, partitioning, component behavior, metadata, and dependency management
Experience with production support, scheduling, logs, restarts, and operational controls
Graph Translation & Reverse Engineering
Proven experience deconstructing legacy Ab Initio applications and documenting functional logic
Ability to identify business rules embedded in transformations, joins, lookups, filters, and aggregations
Strong source-to-target mapping skills
Ability to convert technical graph logic into clear specifications for recreation, modernization, or migration
Experience working with incomplete documentation and legacy systems where knowledge may be tribal or fragmented
Data & Engineering Skills
Strong SQL skills with experience analyzing large datasets
Experience with enterprise data warehouses or data platforms such as Teradata, Oracle, Snowflake, Hadoop, or Databricks
Strong Unix/Linux skills, including shell scripting with KSH, Bash, or similar
Familiarity with job orchestration, file movement, batch controls, data reconciliation, and exception handling
Ability to compare source and target outputs and support functional validation
Business & Communication Skills
Ability to explain complex technical logic to non-technical stakeholders
Strong documentation, communication, and analytical skills
Comfortable working with business teams to validate process intent and expected outcomes
Ability to bridge legacy engineering teams, modern data engineering teams, QA teams, and business stakeholders
Strong problem-solving mindset with the ability to work through ambiguity
Preferred Qualifications
Experience in Financial Services, Banking, Capital Markets, Insurance, or other highly regulated industries
Experience supporting migration from Ab Initio to Databricks, Spark, Snowflake, cloud platforms, or modern data engineering frameworks
Knowledge of AWS, Azure, or GCP data environments
Experience with Ab Initio Metadata Hub, Acquire>It, Query>It, Continuous Flows, ICFF, or meta-programming
Understanding of data governance, lineage, auditability, and regulatory reporting requirements
Familiarity with financial services data domains such as customer, account, transaction, payments, risk, AML, KYC, fraud, finance, or regulatory reporting
Experience creating migration assessment documents, graph inventories, complexity scoring, and modernization roadmaps
Infinitive is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by applicable federal, state, or local law.
Infinitive is required by law in some jurisdictions to include a reasonable estimate of the compensation range for this role. The determination of this range includes various factors not limited to skill set, level, experience, relevant training, and licensure and certifications. Compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range for this role in the U.S. is $124,000 - $190,00.00.
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