Transit Data Engineer

Ryan Mahoney

Director of Product, FirstWho

“It is hard to find engineers who manage the entire pipeline without constant supervision. Most candidates can build a script, but few guarantee reliability when a bus sensor stops sending data at 2 AM. We need people who treat data governance as essential instead of an afterthought. They must communicate clearly when a pipeline breaks instead of hiding the error logs. They need to fix the root cause and secure passenger location data without being told twice.”

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Competency Questions

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Transit Data Engineering & Architecture

Designs, implements, and integrates data pipelines, protocols, and system architectures supporting transit operations with focus on modular components and optimized data flow.

API Development & Integration

Integrates multiple data sources via APIs and implements authentication mechanisms.

Interview round: Hiring Manager Technical

Give me an example of a complex external API integration you implemented for data ingestion.

Positive indicators

Describes retry logic with exponential backoff
Mentions storing raw responses for audit
Explains how they handled partial failures

Negative indicators

No mention of rate limiting or throttling
Assumes API availability is 100%
Hardcoded credentials or insecure storage

Attitude Questions

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Accountability Mindset

The consistent willingness to accept responsibility for the integrity, reliability, and impact of data pipelines and outputs, ensuring transparency and corrective action when standards are not met.

Interview round: Hiring Manager Technical

You discover an error in a dataset that's already been delivered to analysts. They may have already used it in reports. What do you do?

Positive indicators

Communicates proactively, not waiting to be asked
Explains the error and its impact clearly
Offers support to correct any downstream issues

Negative indicators

Waits to see if anyone notices
Minimizes the error's significance
Doesn't offer to help fix downstream impacts

Progression Framework

This table shows how competencies evolve across experience levels. Each cell shows competency at that level.

Transit Data Engineering & Architecture

4 competencies

Competency	Junior	Mid	Senior	Principal
API Development & Integration	Develops basic API endpoints and documents usage using standard templates.	Integrates multiple data sources via APIs and implements authentication mechanisms.	Designs robust API gateways and manages versioning strategies for external consumers.	Sets API governance standards and negotiates data sharing agreements with partners.
Data Pipeline Architecture	Implements predefined ETL jobs and monitors pipeline health using established tools.	Designs modular pipeline components and optimizes data flow for latency and throughput.	Architects scalable data platforms and establishes patterns for error handling and recovery.	Defines organizational data architecture strategy and drives adoption of emerging ingestion technologies.
Data Standards & Modeling	Validates data against existing standards and schemas.	Models data structures to support specific business queries and reporting needs.	Defines enterprise data dictionaries and ensures compliance with industry standards.	Contributes to industry standard bodies and shapes future data interoperability specs.
Real-time Data Processing	Consumes real-time feeds and displays data in dashboards.	Configures stream processing jobs to filter and aggregate live data.	Architects low-latency streaming solutions and handles backpressure scenarios.	Innovates on real-time analytics capabilities to support autonomous and dynamic routing.

Transit Operations, Governance & Analytics

4 competencies

Competency	Junior	Mid	Senior	Principal
Data Governance & Security	Follows data access policies and applies basic encryption methods.	Implements data quality rules and manages user access controls.	Develops governance frameworks and ensures compliance with privacy regulations.	Establishes enterprise data trust policies and leads security incident response strategy.
Equity & Performance Reporting	Compiles data for mandated equity reports.	Analyzes service distribution across demographic groups.	Designs equity metrics frameworks and integrates them into planning processes.	Advocates for data-driven equity policies and aligns them with regional goals.
Fleet & Asset Data Management	Records asset data and updates inventory systems.	Integrates telematics data to monitor vehicle health and utilization.	Optimizes asset lifecycle models using predictive maintenance data.	Plans data infrastructure for electrification and autonomous fleet integration.
Operational Analytics	Generates standard reports on key performance indicators.	Creates dashboards to visualize trends and anomalies in operations.	Develops predictive models to forecast demand and optimize schedules.	Defines analytics strategy to drive long-term operational transformation.

Sample Job Description Content

Transit Data Engineer

Transit Tech

What you'll do

Stream real-time location data – Ingest CAD/AVL feeds via Kafka and Flink to power live tracking maps and ETA displays used by riders daily
Build ETL pipelines for fare systems – Transform daily transaction logs from legacy vendors into clean SQL tables using Airflow for revenue reporting
Implement automated data tests – Monitor battery health and telematics signals with PagerDuty alerts to ensure fleet status accuracy before operations start
Partner with business analysts – Join census data with stop locations in GIS to help planning teams evaluate equity and service coverage quarterly
Parse vehicle diagnostic codes – Decode J1939 and charger status data using Python to maintain accurate maintenance records and EV infrastructure views
Model transit data standards – Enforce GTFS and GTFS-RT specifications across feeds so third-party apps and internal dashboards consume consistent schema
Mentor junior engineers – Review code for pipeline reliability and share best practices on latency management during weekly team syncs
Optimize complex SQL queries – Refine warehouse performance to reduce dashboard load times and support faster recovery during operational incidents

Who you are

You see data as a reflection of real people moving through their cities. You practice active listening when talking to drivers and planners because you know their on-the-ground experience reveals what raw numbers cannot. You approach every pipeline with intellectual humility and recognize that your initial model might need adjustment to match operational reality.

You communicate clearly about what is possible and set professional boundaries when requests threaten data integrity or safety. You have the professional courage to say no to shortcuts that compromise reliability. When things go wrong you welcome feedback openness and treat errors as opportunities to strengthen the system rather than defend your ego.

You care about who benefits from the insights you build. You bring cultural sensitivity to your engineering decisions and ensure your work serves diverse communities equitably. You want to build tools that help cities move efficiently for everyone.

Why this role will be interesting

You'll solve the hard problem of identity resolution across conflicting vendor systems—CAD, APC, and fare collection often disagree on what a "trip" actually is. Downstream analysts and rider apps depend on your logic to trust the numbers, meaning your code directly shapes how cities understand their own transit networks.

You will own end-to-end reliability for critical data products like real-time arrival accuracy, balancing latency SLOs against data completeness. This responsibility moves you beyond writing scripts to managing the operational health of the data itself.

This isn't isolated backend work; you'll partner closely with analysts to ensure the warehouse serves their questions, not just your pipeline metrics. We value engineers who understand the operational reality behind the rows and columns.

Our Process

Recruiter Screen30 min
Hiring Manager Technical60 min
Peer Technical & Exercise60 min
Cross-Functional Collaboration45 min