Data Engineer

Ryan Mahoney

Ryan Mahoney

Director of Product, FirstWho

“Hiring at this level is hard because you need someone who takes ownership instead of just completing tickets. Most candidates can write code, but few can design a system that survives real traffic without breaking or needing constant fixes. You need to see curiosity where they dug into a failure instead of hiding it, and they must show they understand how their models impact downstream analytics and costs. It is not about knowing every tool but about knowing why a tool fits the problem, because the gap between writing a script and owning a pipeline is wide. You need people who treat data quality as a product feature rather than an afterthought, which requires shifting focus from task completion to system reliability.”

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Competency Questions

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Data Platform & Infrastructure

Design, build, and operate scalable data infrastructure including pipelines, orchestration, streaming systems, and performance optimization for mid-level ownership.

Data Orchestration

Design workflow dependencies, implement retry logic, and optimize scheduling for resource efficiency.

Interview round: Hiring Manager Technical

Describe a workflow where you managed dependencies between multiple data tasks.

Positive indicators

  • Uses directed acyclic graph concepts
  • Implements alerting on workflow failures
  • Plans for catch-up processing
  • Ensures idempotent task design
  • Documents dependency logic

Negative indicators

  • Relies on manual task triggering
  • No handling for task failures
  • Hard-codes execution delays
  • Ignores resource contention
  • No visibility into workflow status

Attitude Questions

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Accountability Mindset

The consistent willingness to accept full responsibility for the reliability, integrity, and outcomes of data infrastructure and pipelines, proactively addressing errors and ensuring trustworthiness of data assets without shifting blame.

Interview round: Hiring Manager Technical

How do you approach ownership when an incident occurs outside of your direct working hours?

Positive indicators

  • Mentions supporting on-call person
  • Follows up next day
  • Respects rotation

Negative indicators

  • Ignores incident completely
  • Takes over without being asked
  • Blames on-call person for issues

Progression Framework

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

Data Platform & Infrastructure

4 competencies

CompetencyJuniorMidSeniorPrincipal
Data Orchestration

Configure basic workflow schedules, monitor job execution, and respond to alerts under guidance.

Design workflow dependencies, implement retry logic, and optimize scheduling for resource efficiency.

Build resilient orchestration frameworks, implement cross-pipeline dependencies, and establish SLA monitoring.

Define orchestration strategy across the organization, evaluate platform alternatives, and drive automation maturity.

Data Pipeline Development

Execute predefined pipeline tasks under supervision, write basic SQL transformations, and troubleshoot common pipeline failures.

Design and implement moderately complex pipelines independently, optimize query performance, and establish monitoring for data flows.

Architect scalable pipeline solutions, mentor junior engineers on best practices, and drive pipeline standardization across teams.

Define enterprise pipeline strategy, evaluate emerging technologies, and establish organization-wide data engineering standards.

Performance Optimization

Identify slow-running queries, apply basic indexing strategies, and follow optimization guidelines.

Analyze execution plans, implement partitioning strategies, and optimize resource utilization.

Lead performance audits, design caching strategies, and establish performance baselines across systems.

Define performance standards, evaluate infrastructure investments, and drive optimization culture organization-wide.

Streaming Architecture

Consume streaming data using predefined patterns, monitor stream health, and handle basic stream failures.

Build streaming pipelines, implement windowing operations, and manage stateful stream processing.

Architect streaming platforms, ensure exactly-once processing guarantees, and optimize stream throughput.

Define streaming strategy, evaluate real-time technologies, and establish event-driven architecture standards.

Data Quality, Governance & Analytics

6 competencies

CompetencyJuniorMidSeniorPrincipal
Data Governance

Document data lineage, maintain metadata catalogs, and follow access control procedures.

Implement governance workflows, manage data classifications, and ensure policy compliance.

Design governance frameworks, lead compliance audits, and establish data ownership models.

Define governance strategy, align with regulatory requirements, and drive data culture transformation.

Data Modeling

Create basic dimensional models, follow established modeling conventions, and document schema changes.

Design star/snowflake schemas, normalize/denormalize appropriately, and optimize for query patterns.

Define modeling standards, lead data architecture reviews, and balance competing stakeholder requirements.

Establish enterprise data modeling strategy, drive data mesh/fabric initiatives, and align models with business strategy.

Data Quality Testing

Write basic data quality tests, execute validation scripts, and report quality issues.

Design comprehensive test suites, implement automated quality gates, and establish quality metrics.

Define quality frameworks, lead quality incident response, and establish data quality SLAs.

Set enterprise quality standards, integrate quality into data culture, and drive continuous improvement.

ML Feature Engineering

Create basic features from existing data, follow feature engineering guidelines, and document feature definitions.

Design feature transformations, implement feature validation, and manage feature versioning.

Architect feature stores, establish feature quality standards, and enable ML team collaboration.

Define feature platform strategy, integrate with MLOps pipelines, and drive feature reuse across teams.

Reverse ETL

Configure basic sync jobs, monitor data delivery, and troubleshoot common sync failures.

Design sync workflows, implement transformation logic, and ensure data consistency across systems.

Architect activation platforms, establish sync patterns, and optimize for operational system constraints.

Define activation strategy, evaluate sync technologies, and drive operational data integration standards.

Team Enablement

Create basic documentation, respond to data requests, and support team members with data access.

Develop self-service tools, conduct training sessions, and establish knowledge sharing practices.

Design enablement programs, mentor team members, and drive adoption of data best practices.

Define enablement strategy, scale knowledge transfer, and build data literacy across organization.