Mid Level Data Science Engineer Jobs
Mid level data science engineer jobs go to professionals ready to own pipelines and models end to end, drive analytical decisions with limited oversight, and guide junior teammates through complex problems. Roles cover on-site, remote, and hybrid settings across Technology & Software, Retail, and Consulting & Professional Services, with employers like Capital One, Walmart, and ZS hiring at this level now.
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About AllTrails
AllTrails is the app for exploring the outdoors. We help people spend more time outside by connecting them to their next adventure. Home to the world’s largest outdoor community, with members and trails around the globe, you can find AllTrails in the Apple App Store, Google Play Store, and at alltrails.com. Every day, our team works to get more people outside and help our members have better experiences on the trail. We lead with our values—positivity, innovation, humility, adaptability, inclusivity, and stewardship—in everything we do. Join us! This is a U.S. based remote position. While this role does not require in-person attendance, we have a strong preference for candidates based in or near one of these cities: San Francisco CA, Portland OR, Seattle WA, Denver CO, and New York NY. Being in one of these cities creates opportunities to connect with teammates through Trail Days, co-working sessions, and local gatherings while still having the flexibility to work remotely. San Francisco employees are highly encouraged to come to the office one day per week.
About The Role
Reporting to the Director of Engineering, Maps & Data, this role will lead the AllTrails’ Data Science & Engineering team, a small and centralized engineering function that cross-functionally supports the business across product pods and partner teams including: data analytics, finance, and marketing. The team’s scope spans scalable data infrastructure, data governance, reliability, compliance, and the development and productionization of machine learning, AI, and algorithmic solutions that power internal operations and member-facing product experiences.
Due to high candidate volume and to ensure our hiring team has sufficient time to give every application the attention it deserves, we have set a closing date of August 20th for this position.
What you'll be doing
Team Leadership & People Management
- Manage & Grow the Team: Lead, hire, and coach a team of data engineers and applied data scientists while fostering team health, professional development, and high performance through active coaching and performance management.
Strategic Business Partnership & AI Strategy
- Stakeholder Alignment: Partner with cross-functional leaders to translate business needs into technical requirements, prioritize initiatives, and balance long-term platform investments with near-term delivery goals.
- Domain & AI Innovation: Serve as a key thought partner on leveraging data, AI, and automation to enhance AllTrails' internal productivity, trail content, map quality, and member personalization experiences.
Data Platform & Engineering Execution
- Pipeline & ML Architecture: Drive the architecture and delivery of scalable batch and streaming pipelines, while scaling the productionization of ML models and algorithmic systems into dependable engineering solutions.
- System Operations & Reliability: Oversee orchestration, transformation, alerting, and data flow across the organization—troubleshooting complex performance bottlenecks and system failures to ensure high system uptime.
Data Governance, Democratization & Platform Operations
- Governance & Self-Service: Champion data reliability, quality, privacy, compliance, and cost efficiency while building tools for data cataloging, documentation, and self-service to democratize data assets.
- Velocity & Continuous Improvement: Create organizational leverage by improving time-to-insight, debugging speed, and deployment velocity, while continuously evaluating new tools and technologies to elevate data capabilities.
What You Bring
Team & People Leadership
- Leadership Experience: 5+ years in data engineering with 3+ years managing data engineering, machine learning, and data science teams with a focus on coaching senior ICs, career development, and building an inclusive, accountable team culture.
- Working Style & Values: An adaptable, self-motivated leader who thrives in fast-paced, ambiguous environments—balancing strategic vision with hands-on technical guidance, humility, empathy, and a passion for the outdoors.
Technical Architecture & Infrastructure
- Platform & Governance: Hands-on experience managing GCP data infrastructure, enforcing data governance, and maintaining production-grade data access patterns, storage, caching, and optimization standards.
- Modern Data Stack: Extensive proficiency in transforming high-volume datasets in columnar SQL warehouses (BigQuery preferred, or Snowflake) using ELT tools (Dataform, dbt) and orchestrators like Apache Airflow.
- Pipeline & Processing: Proven expertise designing complex SQL/Python pipelines using parallelized processing frameworks (Dataflow, Spark, or similar) and core data modeling techniques (dimensional, star schema).
Engineering Practices & AI/ML Ops
- DevOps & SDLC: Deep familiarity with software engineering best practices, including Docker/Kubernetes, CI/CD pipelines, monitoring/observability tools (Datadog, New Relic, etc.), code reviews, and monthly on-call rotation.
- Applied AI/ML: Track record of guiding teams through the full ML lifecycle (MLOps) to support applied use cases like recommendation, ranking, personalization, and classification engines.
Stakeholder Management & Execution
- Cross-Functional Ownership: Skilled at leading across both platform foundations and applied data products, with the business judgment to prioritize competing stakeholder needs.
- End-to-End Delivery: Proven ability to translate complex business requirements into technical specs, collaborating effectively with stakeholders to own projects from inception to completion.
Bonus Points
- Experience working with geospatial data, maps, tiling, graph databases, and/or routing
- Experience working in a multi-cloud environment (GCP + AWS)
- Experience working at a B2C company
- Skilled at managing infrastructure as code (e.g. Terraform)
Perks & Benefits
- Competitive and equitable compensation, including ownership through equity and performance-based bonuses
- Comprehensive health, dental, and vision coverage to support your physical and mental well-being
- Unlimited PTO in addition to company holidays
- Dedicated time once a month to test and improve our product through company-wide no-meeting days
- Fully paid parental leave to birthing and non-birthing parents
- 401k Match & access to financial wellness resources through Origin
- Remote work stipend to help you design a comfortable and productive home office
- Annual learning stipend to invest in your long-term professional growth and skills
- Exclusive discounts on our subscriptions and merchandise for you and your friends & family
- An authentic investment in you as a human being and a professional—we value your identity as much as your output
Nature celebrates you just the way you are and so do we! At AllTrails we’re passionate about nurturing an inclusive workplace that values diversity. It’s no secret that companies that are diverse in background, age, gender identity, race, sexual orientation, physical or mental ability, ethnicity, and perspective are proven to be more successful. We’re focused on creating an environment where everyone can do their best work and thrive.
Offers of employment are contingent upon the successful completion of a background check. AllTrails will consider all qualified applicants, including those with arrest and conviction records, in a manner consistent with all applicable laws, including the Los Angeles Fair Chance Initiative for Hiring Ordinance, the San Francisco Fair Chance Ordinance, and the New York City Fair Chance Act. AllTrails participates in the E-Verify program for all remote locations.
By submitting my application, I acknowledge and agree to AllTrails' Job Applicant Privacy Notice. All official recruiting communications from AllTrails will come from an name@alltrails.com email address. Please review sender details carefully, and do not engage with anyone asking for payment or sensitive financial information as part of the hiring process.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans.
This range represents our good faith estimate for a successful candidate’s starting base salary. Final offers are determined by a blend of factors including specialized skills, experience, and relevant credentials. In addition to base pay, our total rewards package includes an equity stake in the company and a performance-based bonus program.
About AllTrails
AllTrails is the app for exploring the outdoors. We help people spend more time outside by connecting them to their next adventure. Home to the world’s largest outdoor community, with members and trails around the globe, you can find AllTrails in the Apple App Store, Google Play Store, and at alltrails.com. Every day, our team works to get more people outside and help our members have better experiences on the trail. We lead with our values—positivity, innovation, humility, adaptability, inclusivity, and stewardship—in everything we do. Join us! This is a U.S. based remote position. While this role does not require in-person attendance, we have a strong preference for candidates based in or near one of these cities: San Francisco CA, Portland OR, Seattle WA, Denver CO, and New York NY. Being in one of these cities creates opportunities to connect with teammates through Trail Days, co-working sessions, and local gatherings while still having the flexibility to work remotely. San Francisco employees are highly encouraged to come to the office one day per week.
About The Role
Reporting to the Director of Engineering, Maps & Data, this role will lead the AllTrails’ Data Science & Engineering team, a small and centralized engineering function that cross-functionally supports the business across product pods and partner teams including: data analytics, finance, and marketing. The team’s scope spans scalable data infrastructure, data governance, reliability, compliance, and the development and productionization of machine learning, AI, and algorithmic solutions that power internal operations and member-facing product experiences.
Due to high candidate volume and to ensure our hiring team has sufficient time to give every application the attention it deserves, we have set a closing date of August 20th for this position.
What you'll be doing
Team Leadership & People Management
- Manage & Grow the Team: Lead, hire, and coach a team of data engineers and applied data scientists while fostering team health, professional development, and high performance through active coaching and performance management.
Strategic Business Partnership & AI Strategy
- Stakeholder Alignment: Partner with cross-functional leaders to translate business needs into technical requirements, prioritize initiatives, and balance long-term platform investments with near-term delivery goals.
- Domain & AI Innovation: Serve as a key thought partner on leveraging data, AI, and automation to enhance AllTrails' internal productivity, trail content, map quality, and member personalization experiences.
Data Platform & Engineering Execution
- Pipeline & ML Architecture: Drive the architecture and delivery of scalable batch and streaming pipelines, while scaling the productionization of ML models and algorithmic systems into dependable engineering solutions.
- System Operations & Reliability: Oversee orchestration, transformation, alerting, and data flow across the organization—troubleshooting complex performance bottlenecks and system failures to ensure high system uptime.
Data Governance, Democratization & Platform Operations
- Governance & Self-Service: Champion data reliability, quality, privacy, compliance, and cost efficiency while building tools for data cataloging, documentation, and self-service to democratize data assets.
- Velocity & Continuous Improvement: Create organizational leverage by improving time-to-insight, debugging speed, and deployment velocity, while continuously evaluating new tools and technologies to elevate data capabilities.
What You Bring
Team & People Leadership
- Leadership Experience: 5+ years in data engineering with 3+ years managing data engineering, machine learning, and data science teams with a focus on coaching senior ICs, career development, and building an inclusive, accountable team culture.
- Working Style & Values: An adaptable, self-motivated leader who thrives in fast-paced, ambiguous environments—balancing strategic vision with hands-on technical guidance, humility, empathy, and a passion for the outdoors.
Technical Architecture & Infrastructure
- Platform & Governance: Hands-on experience managing GCP data infrastructure, enforcing data governance, and maintaining production-grade data access patterns, storage, caching, and optimization standards.
- Modern Data Stack: Extensive proficiency in transforming high-volume datasets in columnar SQL warehouses (BigQuery preferred, or Snowflake) using ELT tools (Dataform, dbt) and orchestrators like Apache Airflow.
- Pipeline & Processing: Proven expertise designing complex SQL/Python pipelines using parallelized processing frameworks (Dataflow, Spark, or similar) and core data modeling techniques (dimensional, star schema).
Engineering Practices & AI/ML Ops
- DevOps & SDLC: Deep familiarity with software engineering best practices, including Docker/Kubernetes, CI/CD pipelines, monitoring/observability tools (Datadog, New Relic, etc.), code reviews, and monthly on-call rotation.
- Applied AI/ML: Track record of guiding teams through the full ML lifecycle (MLOps) to support applied use cases like recommendation, ranking, personalization, and classification engines.
Stakeholder Management & Execution
- Cross-Functional Ownership: Skilled at leading across both platform foundations and applied data products, with the business judgment to prioritize competing stakeholder needs.
- End-to-End Delivery: Proven ability to translate complex business requirements into technical specs, collaborating effectively with stakeholders to own projects from inception to completion.
Bonus Points
- Experience working with geospatial data, maps, tiling, graph databases, and/or routing
- Experience working in a multi-cloud environment (GCP + AWS)
- Experience working at a B2C company
- Skilled at managing infrastructure as code (e.g. Terraform)
Perks & Benefits
- Competitive and equitable compensation, including ownership through equity and performance-based bonuses
- Comprehensive health, dental, and vision coverage to support your physical and mental well-being
- Unlimited PTO in addition to company holidays
- Dedicated time once a month to test and improve our product through company-wide no-meeting days
- Fully paid parental leave to birthing and non-birthing parents
- 401k Match & access to financial wellness resources through Origin
- Remote work stipend to help you design a comfortable and productive home office
- Annual learning stipend to invest in your long-term professional growth and skills
- Exclusive discounts on our subscriptions and merchandise for you and your friends & family
- An authentic investment in you as a human being and a professional—we value your identity as much as your output
Nature celebrates you just the way you are and so do we! At AllTrails we’re passionate about nurturing an inclusive workplace that values diversity. It’s no secret that companies that are diverse in background, age, gender identity, race, sexual orientation, physical or mental ability, ethnicity, and perspective are proven to be more successful. We’re focused on creating an environment where everyone can do their best work and thrive.
Offers of employment are contingent upon the successful completion of a background check. AllTrails will consider all qualified applicants, including those with arrest and conviction records, in a manner consistent with all applicable laws, including the Los Angeles Fair Chance Initiative for Hiring Ordinance, the San Francisco Fair Chance Ordinance, and the New York City Fair Chance Act. AllTrails participates in the E-Verify program for all remote locations.
By submitting my application, I acknowledge and agree to AllTrails' Job Applicant Privacy Notice. All official recruiting communications from AllTrails will come from an name@alltrails.com email address. Please review sender details carefully, and do not engage with anyone asking for payment or sensitive financial information as part of the hiring process.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans.
This range represents our good faith estimate for a successful candidate’s starting base salary. Final offers are determined by a blend of factors including specialized skills, experience, and relevant credentials. In addition to base pay, our total rewards package includes an equity stake in the company and a performance-based bonus program.
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Find JobsMid Level Data Science Engineer Job Market
Who's Hiring
Top Industries Hiring
- Technology & Software13
- Retail10
- Consulting & Professional Services9
- Banking & Financial Services5
- E-Commerce & Online Marketplaces5
Mid Level Data Science Engineer Jobs: Frequently Asked Questions
How do I get a mid level data science engineer job?
Lead with ownership. Hiring managers at this level want to see that you have driven a project from problem definition through deployment, not just contributed to one. Highlight specific models you built, decisions you made independently, and measurable outcomes you can claim. Tailor your resume to show depth in a domain, such as NLP, computer vision, or recommendation systems, rather than listing every tool you have touched.
Which companies hire mid level data science engineers?
Companies hiring mid level data science engineers right now include Capital One, Walmart, and ZS, based on current listings on Migrate Mate as of August 2026. Hiring at this level comes from a wide range of employers, including large technology firms, financial services companies, healthcare organizations, and growth-stage startups building out their core data teams.
Are there remote mid level data science engineer jobs?
Yes, remote work is common at this level. About 43% of mid level data science engineer openings are remote or hybrid as of August 2026, reflecting strong employer comfort with distributed data teams. Many companies hire nationally for these roles, which means your location rarely limits the number of positions you can apply to.
How do I move up to a mid level data science engineer role?
The path from entry level to mid level is built on demonstrated ownership. Early-career engineers who take on end-to-end projects rather than isolated tasks, seek feedback from senior colleagues, and document measurable impact tend to reach mid level faster. Deepening expertise in one domain, such as time-series forecasting or ML infrastructure, signals readiness for the greater autonomy that mid level roles require.
Which industries hire the most mid level data science engineers?
Mid Level data science engineer roles concentrate in Technology & Software, Retail, and Consulting & Professional Services, based on current listings on Migrate Mate as of August 2026. Those sectors generate large volumes of structured and unstructured data that require engineers who can independently design, build, and maintain production-grade models rather than just run exploratory analyses.