Elevate
Data Engineer
Elevate - Manager
United States · Remote
Technical/Engineering
$100,000 - $155,000 / year
0
8
hours ago
ABOUT ELEVATE:
Elevate is a full-service consulting firm that inspires high-performing organizations to find their limits and push past them. With expertise in brand consulting, sales strategy, data-driven insights, and talent optimization, Elevate gives its clients a competitive edge in the fight for people’s precious time and attention. Established in 2018, Elevate set out to help sports teams and leagues spark innovation and drive performance. In the years since, the world of sports has transformed, today standing at the convergence of media, entertainment, and consumer brands, with Elevate supporting some of the world’s most ambitious businesses across these sectors. Elevate’s proprietary technology, data sources, and software products combined with our thoughtful insights, and people-centric approach give clients a 360-degree view of their customers, underpinning intelligent decision-making on marketing spend, growth strategy, and more.
Our team of 700+ employees spans the globe with in 20 locations worldwide. We value recruiting diverse individuals to our team to bring new perspectives to our company and look forward to learning more about you in the recruitment process. To learn more and see what we’ve been up to, follow Elevate on X, LinkedIn, and Instagram.
Where you come in
We are hiring a Data Engineer to help build the data infrastructure behind Elevate's next generation of products and internal platforms.
This role is focused on the foundation: data lakes, data pipelines, ingestion workflows, transformation logic, data quality, observability, and the operating practices that make data products trustworthy over time. You will work across product areas like data products, reporting workflows, and automation tools, where teams need clean, repeatable pipelines instead of one-off data pulls or semi-custom integrations for every client.
This is a hands-on engineering role for someone who enjoys building the plumbing that makes product and analytics work possible. The right person can take messy source systems, partner data, business rules, and ambiguous reporting needs, then turn them into reliable data flows that engineering, product, analytics, and business teams can actually depend on.
What You'll Own
- Data infrastructure for new and existing Elevate products, with an emphasis on scalable data lakes, warehouses, pipelines, and data services.
- ETL and ELT pipelines that ingest, clean, transform, validate, and deliver data from partner systems, client sources, internal applications, and third-party providers.
- Data modeling and transformation logic that supports data products, reporting automation, business intelligence, and future product data needs.
- Pipeline reliability, monitoring, alerting, and recovery patterns so data issues are visible before they become product or stakeholder problems.
- Data quality rules, validation checks, schema management, and reconciliation workflows that improve trust in downstream reporting and product outputs.
- Practical documentation for data sources, transformations, ownership, lineage, and operational runbooks.
- Collaboration with product engineers, analytics teams, ML engineers, and business stakeholders to translate data needs into maintainable technical systems.
- Clear recommendations on when a data request should become platform infrastructure, when it should remain a one-off analysis, and when the business case does not justify the build and maintenance cost.
What You'll Bring
- 4+ years of experience in data engineering, analytics engineering, backend/data platform engineering, or a closely related role.
- Strong SQL and Python skills, with experience building production-grade data pipelines.
- Experience designing and operating data lakes, data warehouses, ETL/ELT workflows, batch processing, and scheduled data jobs.
- Experience with cloud data infrastructure, especially AWS services such as S3, Glue, Lambda, Step Functions, ECS, Redshift, Athena, RDS, EventBridge, or similar tools.
- Ability to model messy real-world business data into clean, understandable, and maintainable structures.
- Strong instincts for data quality, validation, observability, lineage, and operational support.
- Comfort working with APIs, flat files, partner exports, databases, and other source systems that are not always clean or consistent.
- Ability to partner with software engineers so data pipelines and product features fit together cleanly.
- Ability to explain data architecture, tradeoffs, risks, and maintenance needs to technical and non-technical stakeholders.
- Pragmatic engineering judgment. You know when to build a reusable pipeline, when to create a lightweight first version, and when to push for clearer business value before committing engineering time.
Preferred experience
- Experience with AWS data and analytics services in a production environment.
- Experience with orchestration tools such as Airflow, Dagster, Prefect, Step Functions, or similar workflow systems.
- Experience with transformation and analytics engineering tools such as dbt or similar frameworks.
- Experience supporting data products in sports, entertainment, ticketing, media, sponsorship, research, or consumer analytics.
- Experience with data contracts, schema evolution, data catalogs, lineage tooling, and data observability platforms.
- Experience partnering with ML engineers or data scientists on feature pipelines, model inputs, evaluation datasets, or AI-assisted product workflows.
- Experience building multi-tenant or client-specific ingestion patterns without turning every client into a fully custom implementation.
How You'll Make an Impact
- Help Elevate move from isolated data projects to reusable data infrastructure that can support multiple products and business teams.
- Build the data foundation for product analytics and reporting so reporting and analytics work can scale beyond custom client-by-client setup.
- Support emerging AI and data automation workflows by turning vendor, partner, and internal data needs into clear pipelines, ownership models, and supportable infrastructure.
- Strengthen audience and data workflows by improving the reliability, freshness, and trustworthiness of the data that powers audience, persona, reporting, and AI-assisted workflows.
- Reduce the long-term maintenance burden of data products by designing pipelines with monitoring, ownership, and recovery paths from the beginning.
- Help leadership understand the difference between build cost and ongoing run cost for data initiatives.
- Partner with engineers and product leaders to decide when a data platform investment is worth making and what tradeoffs come with it.
- Create reusable patterns for onboarding new data sources, validating data, and serving downstream products.
Your Journey: First 90 Days
First 30 Days
- Learn Elevate's current product landscape, including reporting workflows, data automation needs, audience and data workflows, and related internal tools.
- Map the current data sources, pipelines, manual processes, vendor handoffs, and known quality issues.
- Understand where data work is currently handled by product engineers, analysts, vendors, or one-off scripts.
- Identify the highest-risk data infrastructure gaps that create delivery risk, manual effort, or low trust in outputs.
Days 31-60
- Own a focused data infrastructure improvement, such as a source ingestion pipeline, validation framework, data quality dashboard, or repeatable onboarding pattern for a high-priority product area.
- Define what production readiness looks like for the pipeline: monitoring, alerts, data freshness, schema handling, ownership, and documentation.
- Partner with product and engineering stakeholders to separate reusable platform work from one-off reporting or analysis needs.
- Create a practical roadmap for the data infrastructure work needed to support reporting workflows, data automation products, and audience and data workflows over the next few quarters.
Days 61-90
- Deliver a production-ready data pipeline or infrastructure improvement that reduces manual effort or improves trust in a key product area.
- Establish repeatable patterns for data ingestion, validation, observability, and handoff to downstream systems.
- Help leadership understand ongoing support needs, operational risk, and capacity implications for data-heavy product bets.
- Become the team's go-to owner for data infrastructure questions across product engineering.
Why This Role Matters Now
Elevate is seeing more requests that sound simple on the surface but depend on serious data infrastructure underneath: new reporting layers, data lake builds, media automation, analytics client onboarding, and audience and data workflows.
Elevate is seeing more requests that sound simple on the surface but depend on serious data infrastructure underneath: new reporting layers, data lake builds, media automation, analytics client onboarding, and audience and data workflows.
The business needs someone who can help turn those requests into durable systems instead of fragile one-off builds. This role gives Elevate a dedicated owner for the data foundation that product engineering, analytics, ML, and business teams need to build on.
The Technology You'll Work With
- SQL and Python
- AWS data services such as S3, Glue, Lambda, Step Functions, Redshift, Athena, RDS, EventBridge, and related services
- ETL and ELT orchestration tools
- Data warehouse and data lake architecture
- APIs, partner data feeds, flat files, and internal application databases
- Data validation, observability, lineage, and documentation tooling
- Product systems across data products, reporting workflows, automation tools, and future Elevate data products
Our Product Engineering Principles
- Customer Obsession: We start with the customer and work backward. We don't build features; we solve customer and business problems.
- Ownership: Teams own outcomes, not just outputs. Success means business impact and customer delight, not shipping on schedule.
- Simplicity: We choose architectural and product simplicity over complexity. Complex solutions are harder to maintain, evolve, and explain.
- Data-Driven Decisions: We measure what matters and use data to guide our product development and business decisions.
- Rapid Learning: We implement quick feedback loops, focus on learning through experimentation, and value progress over perfection.
- Transparency: We invite visibility and are transparent with where we're spending our time.
POSITION AND BENEFITS DETAILS:
- This position is fully remote
- Full Time – Exempt
- Anticipated Base Salary: $100,000 - $155,000 (this position is bonus eligible)
- Medical, Dental, Vision, Life, Short-Term & Long-Term Disability Insurance + FSA, HSA, and more
- 401k Employer Match after meeting eligibility requirements
- 14 Paid Holidays
- Unlimited PTO
- Paid Parental Leave
This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. We may ultimately pay more or less than the posted range, and the range may be modified in the future. An employee’s pay position within the salary range will be based on several factors including, but limited to, relevant education, qualifications, certifications, experience, skills, seniority, geographic location, performance, shift, travel requirements, sales or revenue-based metrics, any collective bargaining agreements, and business or organizational needs.
This position is open to all qualified candidates. If you need assistance or an accommodation due to a disability in connection with the application process, you may contact us at [email protected]
We are proud to be an equal opportunity/veterans/disabled/ LGBT employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All employment is decided based on qualifications, merit, and business need, without regard to race, color, religion, gender, sexual orientation, national origin, disability status, protected veteran status, genetic information, or any other characteristic protected by applicable law.
Job Questions:
How many years of professional experience do you have in this role or a closely related role?
Why are you interested in this role at Elevate?
Tell us about a data pipeline, warehouse, lake, or ETL/ELT workflow you built or maintained. What tools did you use?
How do you handle data quality issues, schema changes, monitoring, or failed jobs?
Do you have current/unrestricted work authorization to work in the US?
Will you now or in the future require sponsorship for employment visa status to work in the US?
