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Conduct full-cycle technical recruiting for data engineering, data architecture, and AI/ML roles. Source and screen candidates through technical discussions, manage multiple changing searches, coordinate interviews and offers, maintain ATS records, and build talent pipelines. Partner with hiring managers to clarify requirements, communicate search progress, and recruit consultants with data, AI, and consulting expertise.

Build and maintain Databricks data pipelines using Python, PySpark, Spark SQL, and Delta Lake. Ingest data from databases, APIs, files, and cloud storage; implement medallion architecture; optimize Spark workloads; add data quality checks; manage schema changes; and monitor workflows. The role also involves Git-based development, documentation, code reviews, CI/CD participation, and client-facing communication. It is hybrid and based at client sites in McLean, Virginia.

Designs, builds, and maintains scalable data pipelines and ETL processes using Databricks. Implements CI/CD and infrastructure automation with Jenkins and Terraform, develops 3NF, dimensional, and Data Vault models, optimizes performance, and ensures data quality. Collaborates with data scientists, analysts, and software engineers, documents solutions, establishes best practices, and mentors junior team members.

Drive net-new sales of data and AI consulting solutions to commercial and public sector clients. Generate leads, build Databricks co-selling partnerships, manage pipelines, lead discovery and proposal activities, navigate commercial and public procurement processes, and close contracts. The role requires consultative selling expertise, strong data and cloud acumen, executive-level communication, and the ability to sell modern data platform transformations.

Leads enterprise Databricks platform architecture and Unity Catalog enablement for governed, scalable, cost-efficient data access. Defines platform vision, governance, access controls, metadata, lineage, observability, FinOps, APIs, and Lakehouse data architectures. Guides Spark, PySpark, and SQL pipeline design while partnering with engineering, data science, risk, and architecture teams. Mentors technical teams, establishes reusable standards, and drives enterprise adoption of Databricks as a managed data platform.

Leads the technical vision and architecture for enterprise event-driven data platforms using Apache Kafka and Temporal. Designs high-throughput streaming systems, durable workflows, data contracts, schema governance, Lakehouse architectures, distributed computing workloads, observability, and FinOps practices. Establishes engineering standards, leads architecture reviews, mentors senior engineers, and aligns distributed systems initiatives with executive priorities.

Develop and govern enterprise ontologies and semantic data models aligned with data strategy. Map disparate data sources, support data integration, promote semantic technologies, and collaborate with technology, machine learning, and product teams. Apply W3C standards, manage ontology lifecycles, test semantic systems, establish modeling best practices, and support consulting projects. The role requires metadata and linked-data experience, with preferred knowledge of graph databases, cloud platforms, ontology tools, SQL, and SPARQL.

Designs and implements AI-enabled business process transformations for clients. Responsibilities include prompt and context engineering, MCP tool and API integrations, agentic workflow design, knowledge architecture, documentation, evaluation, testing, governance, and human-in-the-loop controls. The role requires translating ambiguous business challenges into reliable AI systems, collaborating with executives and technical teams, and developing scalable operating models using modern AI tools.

Recent graduates will join Infinitive’s technology consulting team, gaining hands-on experience with AWS, Databricks, data analysis, data engineering, application development, and AI. Responsibilities include developing modern technology solutions, applying industry best practices, building consulting skills, and pursuing AWS and Databricks certifications. Candidates need a technical degree, baseline data and application development experience, AI tool familiarity, strong analytical and communication skills, self-motivation, and leadership abilities.

Design and scale event-driven data platforms using Apache Kafka and Temporal. Build batch and streaming ETL/ELT pipelines with Python, SQL, and Spark; develop data schemas, validation, contracts, and quality controls; optimize warehouse and lakehouse models; and implement reliable, observable workflows. Collaborate with software, machine learning, and analytics teams on distributed systems, CI/CD, and production data governance.