About this role
Position Summary
Scicom Infrastructure Services is seeking a Data Engineer / Business Analyst to support a large-scale Databricks implementation. This hybrid role will bridge business, contracting, data-governance, and engineering teams to define business requirements, construct data contracts, map source data, and develop reliable Databricks data pipelines.
The successful candidate must have hands-on experience with the Open Contracting Data Standard (OCDS) and understand how contracting and procurement information is structured across planning, tender, award, contract, and implementation stages. OCDS provides a standardized model for publishing and analyzing data throughout the public-contracting process and uses defined schemas, codelists, releases, records, and packages.
This individual will work closely with business stakeholders, procurement subject-matter experts, data architects, Databricks engineers, governance teams, and program leadership to translate complex business and contracting requirements into enforceable technical specifications and production-ready data products.
Key Responsibilities
Data Contracts and Business Analysis
- Lead requirements-gathering sessions with procurement, contracting, program, analytics, governance, and technical stakeholders.
- Define, construct, document, and maintain data contracts between data producers and consumers.
- Establish data-contract requirements covering:
Dataset purpose and ownership
- Source and target systems
- Schemas, fields, and data types
- Required and optional attributes
- Business definitions and transformation rules
- Data-quality expectations
- Validation and reconciliation rules
- Refresh frequency and delivery schedules
- Versioning and schema-evolution requirements
- Security classifications and access controls
- Service-level expectations
- Issue ownership and change-management procedures
- Translate business requirements into user stories, acceptance criteria, process flows, data mappings, interface specifications, and technical requirements.
- Conduct source-system analysis, data profiling, gap assessments, and source-to-target mapping.
- Identify differences between existing procurement data and required OCDS structures.
- Facilitate agreement among data owners, producers, consumers, architects, and governance teams.
- Maintain traceability from business requirements through data models, engineering implementation, testing, and acceptance.
- Evaluate requested changes for downstream effects on data products, reports, integrations, and analytical use cases.
- Support backlog refinement, sprint planning, demonstrations, testing, and stakeholder acceptance.
OCDS Responsibilities
- Apply the Open Contracting Data Standard to procurement and public-contracting datasets.
- Map source-system data to appropriate OCDS fields and structures.
- Work with data across the contracting lifecycle, including:
Planning
- Tender
- Award
- Contract
- Implementation
- Develop and maintain mappings for OCDS releases, records, release packages, record packages, identifiers, organizations, parties, items, milestones, documents, transactions, amendments, and related contracting elements.
- Interpret and apply OCDS schemas, codelists, validation rules, and implementation guidance.
- Determine whether standard OCDS fields meet project requirements or whether documented extensions are necessary.
- Support the construction of complete contracting records from multiple transactional releases.
- Establish rules for handling amendments, updates, cancellations, corrections, and historical changes.
- Validate transformed data against applicable OCDS JSON schemas.
- Identify missing, invalid, inconsistent, or nonconforming procurement data and work with stakeholders to resolve deficiencies.
- Document assumptions, business rules, mappings, extensions, and exceptions.
- Support the publication, exchange, analysis, or internal use of standardized contracting data.
Databricks Data Engineering
- Design, develop, test, and maintain data pipelines using Databricks, Apache Spark, PySpark, Python, and SQL.
- Build ingestion and transformation pipelines for structured and semi-structured procurement data.
- Process JSON, CSV, XML, Parquet, relational database, API, and file-based data sources.
- Implement Bronze, Silver, and Gold data layers using medallion architecture.
- Build normalized, dimensional, analytical, and OCDS-aligned data models.
- Use Delta Lake capabilities for schema enforcement, schema evolution, versioning, auditability, and reliable processing.
- Develop reusable frameworks for mapping source procurement data into OCDS-compatible outputs.
- Implement batch and incremental ingestion patterns.
- Use Databricks Workflows, notebooks, jobs, Auto Loader, Delta Live Tables or Lakeflow capabilities, as appropriate.
- Develop REST API integrations for source ingestion and downstream data delivery.
- Implement automated data-quality, reconciliation, completeness, and conformity checks.
- Support Unity Catalog implementation for metadata, ownership, lineage, access control, and data discovery.
- Optimize Spark jobs, SQL queries, clusters, partitioning, file sizes, and data layouts.
- Participate in code reviews, automated testing, CI/CD, deployment, monitoring, and production support.
- Investigate pipeline failures, data discrepancies, and performance issues.
Data Quality and Governance
- Define measurable quality rules for accuracy, completeness, validity, timeliness, consistency, and uniqueness.
- Develop validation controls for required OCDS fields, identifiers, dates, amounts, currencies, organizations, classifications, and contracting relationships.
- Create dashboards or reports that show data-contract compliance and data-quality results.
- Establish processes for detecting and managing schema drift.
- Document data lineage from original procurement systems through Databricks transformations and downstream products.
- Work with governance teams to assign data owners, stewards, classifications, retention requirements, and access policies.
- Ensure sensitive procurement and supplier information is handled according to security and privacy requirements.
- Support auditability through documented rules, version-controlled mappings, validation results, and change histories.
Required Qualifications
- Bachelor’s degree in computer science, information systems, data analytics, business analysis, engineering, public administration, supply-chain management, or a related field.
- At least five years of combined data-engineering, data-analysis, business-analysis, or data-integration experience.
- Hands-on experience implementing or working with the Open Contracting Data Standard .
- Demonstrated experience constructing, documenting, negotiating, or maintaining data contracts.
- Experience mapping procurement or contracting data to OCDS schemas.
- Strong knowledge of OCDS releases, records, schemas, codelists, identifiers, contracting stages, and validation practices.
- At least three years of hands-on experience with Databricks.
- Strong experience with:
Apache Spark
- PySpark
- Python
- SQL
- Delta Lake
- ETL and ELT pipelines
- Data modeling
- JSON and JSON Schema
- REST APIs
- Data-quality validation
- Source-to-target mapping
- Experience gathering requirements and translating them into implementable engineering specifications.
- Ability to communicate effectively with technical and nontechnical stakeholders.
- Experience writing user stories, acceptance criteria, business rules, data dictionaries, interface specifications, and process documentation.
- Strong analytical, troubleshooting, facilitation, and documentation skills.
- Experience working within Agile delivery teams.
Preferred Qualifications
- Experience with government procurement, public-sector contracting, grants, acquisition, supplier, or financial data.
- Experience implementing OCDS extensions or tailoring OCDS for specific organizational requirements.
- Familiarity with procurement classifications, organizational identifiers, tender processes, awards, amendments, milestones, transactions, and contract implementation.
- Databricks Certified Data Engineer Associate or Professional certification.
- Experience with Unity Catalog, Delta Live Tables, Lakeflow, Databricks Workflows, or Structured Streaming.
- Experience with Microsoft Azure, AWS, or Google Cloud.
- Experience with Azure Data Factory, ADLS Gen2, AWS Glue, Amazon S3, Snowflake, dbt, Kafka, Airflow, or Power BI.
- Experience using JSON Schema validation tools and automated data-quality frameworks.
- Knowledge of metadata management, master-data management, data lineage, and reference-data governance.
- Experience with CI/CD, Git, Azure DevOps, GitHub Actions, Jenkins, or Terraform.
- Experience supporting large federal, state, local-government, or regulated-enterprise data programs.
- Familiarity with federal acquisition, procurement, reporting, transparency, or open-data requirements.
Core Competencies
- Ability to operate equally well in technical engineering and business-analysis discussions.
- Strong understanding of how data contracts create accountability between data producers and consumers.
- Ability to convert complex procurement processes into clear data structures and transformation rules.
- Attention to detail when interpreting schemas, codelists, business definitions, and validation requirements.
- Strong stakeholder-facilitation and conflict-resolution skills.
- Ability to identify gaps and ambiguities before they become engineering defects.
- Commitment to documentation, traceability, governance, and data quality.
- Ability to work effectively within a large, multidisciplinary Databricks team.