Manager, Data Platforms

Salary Range: $140,000.00 To $160,000.00 Annually TRANSIT TECHNOLOGIES Job Description Manager, Data Platforms Department Product Development Reports To Vice President of Infra, Data and AI engineering Scope All Transit Technologies operating companies (OpCos) Location Remote, US Direct Reports Data platform engineers / data engineers (team to scale with mandate) Position Summary The Manager, Data Platforms owns the strategy, architecture, and operational health of Transit Technologies' enterprise data platform, built on Databricks, across all operating companies (OpCos). This role is accountable for turning a fragmented, product-by-product data landscape into a single, well-governed, cost-efficient platform that reliably powers analytics, reporting, and production AI/ML and agentic systems. The role sits at the intersection of data engineering, platform engineering, and security, and requires equal comfort setting multi-year architecture direction and getting hands-on when a pipeline breaks at 2 a.m. This is an individual-contributor-to-manager hybrid role in its first year: the person will both build and lead, standing up team structure and process as the platform's footprint grows across FASTER, Ecolane, and other OpCo products. Key Responsibilities 1. Databricks Architecture & Platform Strategy Own the end-to-end Databricks architecture — workspace topology, Unity Catalog, compute/cluster policies, and workload isolation — as the single reference architecture for every OpCo, and maintain a multi-year roadmap that consolidates today's 250+ single-tenant Azure deployments into one shared, scalable platform. Act as architectural authority for OpCo onboarding, acquisition integration, and product-line expansion so every new environment inherits platform standards, and partner with product and AI engineering leadership to make agentic and AI workloads first-class citizens. 2. Data Pipelines & Engineering Own the design standards, orchestration patterns, and reliability bar for all ELT/ETL pipelines — batch, streaming, and change-data-capture — and establish reusable frameworks, templates, and CI/CD patterns so OpCo teams build on shared tooling. Drive data quality and contract standards (schema versioning, validation, lineage) so reporting, analytics, and AI agents can trust the data without manual reconciliation. 3. Data and Platform Governance Own the enterprise data governance framework — classification, ownership/stewardship, cataloging, and metadata management — across all OpCos, and chair a governance operating cadence (policy review, exception handling, escalation) with clear accountability. Define platform-wide standards for environment provisioning, access control, change management, and tooling approval so every OpCo operates within a consistent, auditable framework. Own a single system of record for data products, pipelines, and datasets — what exists, who owns it, and its certification status — and run periodic health and compliance reviews with OpCo leads to track drift. Own end-to-end observability — pipeline monitoring, job SLAs, data freshness/quality alerting, and cost/utilization dashboards — with defined SLIs/SLOs (freshness, completeness, success rate, latency), automated alerting, and a clear on-call/escalation model. Build the reporting layer that gives engineering and executive leadership real-time visibility into platform health across all OpCos, not just the OpCo reporting an incident. 6. Data Security Own data security posture across the platform — encryption, access control (RBAC/ABAC), secrets and key management, and PII/PHI handling — and enforce least-privilege through Unity Catalog and workspace permissions consistently across every OpCo tenant. Lead data security incident response for platform events and drive post-incident remediation and control improvements. 7. Data Availability Own platform-wide availability, backup, and disaster recovery — RPO/RTO targets by data tier and tested recovery runbooks — and design for multi-region/multi-tenant resilience so a single OpCo or workspace incident cannot cascade platform-wide. Manage capacity planning proactively (Azure/AWS/Databricks) to prevent availability incidents driven by resource constraints rather than design flaws. 8. Data Tool Selection & Evaluation Own the evaluation and selection of data tooling across the stack — BI (e.g., SSRS replacements such as Sigma Computing, Bold Reports), catalog, quality, and orchestration — with a clear, repeatable framework, and maintain a rationalized inventory that retires redundant or legacy tools as OpCos consolidate. Run structured proof-of-concept and vendor evaluations, balancing capability, total cost of ownership, and integration effort with Databricks and Unity Catalog. 9. Cost Optimization Own Databricks and cloud (Azure/AWS) cost governance — compute right-sizing, job/cluster policies, storage tiering, and chargeback/showback by OpCo — and partner with MSP and cloud vendors to optimize spend as OpCos consolidate onto shared capacity. Report platform unit economics (cost per pipeline, per workload, per OpCo) to leadership and tie optimization initiatives to measurable savings. Cross-Cutting & Leadership Responsibilities Serve as the single owner of data platform strategy across all ten (and growing) Transit Technologies operating companies, ensuring one architecture, one governance model, and one roadmap rather than ten parallel efforts. Represent the data platform in the enterprise AI governance program, contributing to and consuming the broader governance document suite (data, security, model, and agent policies). Build and mentor a data platform engineering team as scope and headcount grow, establishing on-call rotations, code review standards, and career development paths. Partner cross-functionally with product engineering, security, FP&A, and OpCo leadership to align platform investment with business priorities. Communicate platform status, risk, and roadmap clearly to executive stakeholders, including the CTO and CEO, in both written and verbal formats. Required Qualifications 7+ years in data engineering or data platform roles, with 2+ years in technical leadership capacity. Deep hands-on experience with Databricks (Unity Catalog, Delta Lake, job orchestration, cluster/compute management) at production scale. Proven experience owning data governance and security programs, including access control, classification, and compliance-driven data handling. Track record of driving platform standardization across multiple business units, products, or acquired companies. Excellent written and verbal communication skills, with the ability to translate technical tradeoffs into business terms for executive audiences.