
Sporting goods retail chain
Senior Data Engineer - Athlete (REMOTE)
필수 스킬
Python
SQL
Spark
At DICK’S Sporting Goods, we believe in how positively sports can change lives. On our team, everyone plays a critical role in creating confidence and excitement by personally equipping all athletes to achieve their dreams. We are committed to creating an inclusive and diverse workforce, reflecting the communities we serve.
If you are ready to make a difference as part of the world’s greatest sports team, apply to join our team today!
OVERVIEW:
On the Athlete Data team, we build the data foundation that powers personalized customer experiences, loyalty programs, and marketing insights. Our work spans multiple systems and partners, and—just like all great teams—it requires trust, clarity, discipline, and collaboration.
We’re looking for a Senior Data Engineer who values thoughtful problem‑solving, clean design, and strong data hygiene. Someone who can move between conceptual modeling and hands‑on implementation, and who enjoys working in an environment where everyone succeeds through the strengths they bring to the team.
Capable engineers thrive here because we emphasize transparency, respect, steady improvement, and shared wins.
Job Purpose:
As a Senior Data Engineer on the Athlete team, you’ll work across a diverse and evolving data ecosystem that supports critical marketing functions. The role requires the ability to operate both deep within complex data domains and broadly across many systems, partners, and vendors. You’ll turn loosely defined business problems into clear, executable data solutions, helping bring structure and momentum to ambiguous work.
You’ll play a key role in designing, building, and evolving data pipelines and models—especially in environments that involve sensitive customer data, identity resolution, and the unification of disparate sources. Success in this role depends on strong technical fundamentals, sound data judgment, and the ability to collaborate effectively across analytics, engineering, marketing, and product teams.
Responsibilities:
Build High‑Quality Pipelines
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Develop reliable data pipelines using SQL, Python, Databricks/Spark.
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Improve and modernize existing pipelines for clarity, performance, and maintainability.
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Add tests, documentation, lineage, and observability to strengthen trust.
Model Data Across Many Systems
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Translate complex, multi‑source schemas into clear conceptual and physical models.
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Handle grain alignment, key logic, and definition clarity with care.
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Design models that scale and reduce friction for analysts, product, and downstream users.
Protect Customer Data
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Apply disciplined data hygiene and privacy-first patterns.
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Safely move and store sensitive customer data in compliance with organizational standards.
Collaborate Across the Field
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Partner with analysts, product managers, marketing teams, data scientists, vendor teams, and other engineers.
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Communicate assumptions, trade‑offs, and decision points clearly.
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Bring clarity to ambiguous problems and help guide the team toward the best path forward.
Lift the Team
Like any high-performing team, we win together:
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Share knowledge, document meaningfully, and help strengthen team patterns.
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Support teammates through code reviews, pairing, and problem‑solving.
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Use AI‑assisted tools to accelerate documentation, test generation, and iteration.
What Helps You Succeed Here
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Strong SQL and Python fundamentals
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Experience with Databricks/Spark or similar large‑scale platforms
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Clarity in thinking about data models and transformations
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Detail‑orientated and disciplined data hygiene
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Experience in refactoring codebase, cataloging/data governance, and agile development are a plus
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Ability to break down complex problems and communicate them clearly
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Curiosity and willingness to learn new tools/contexts quickly
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Team-first mindset — you share, collaborate, and elevate others
QUALIFICATIONS:
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Bachelor's Degree or equivalent level preferred
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General Experience: Experience enables job holder to deal with the majority of situations and to advise others (Over 3 years to 6 years)
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Managerial Experience: Basic experience of coordinating the work of others (4 to 6 months)
VIRTUAL REQUIREMENTS:
At DICK’S, we thrive on innovation and authenticity. That said, to protect the integrity and security of our hiring process, we ask that candidates do not use AI tools (like ChatGPT or others) during interviews or assessments.
To ensure a smooth and secure experience, please note the following:
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Cameras must be on during all virtual interviews.
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AI tools are not permitted to be used by the candidateduring any part of the interview process.
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Offers are contingent upon a satisfactory background check which may include ID verification.
If you have any questions or need accommodations, we’re here to help. Thanks for helping us keep the process fair and secure for everyone!
Targeted Pay Range: $83,000.00 - $138,200.00. This is part of a competitive total rewards package that could include other components such as: incentive, equity and benefits. Individual pay is determined by a number of factors including experience, location, internal pay equity, and other relevant business considerations. We review all teammate pay regularly to ensure competitive and equitable pay.DICK'S Sporting Goods complies with all state paid leave requirements. We also offer a generous suite of benefits. To learn more, visit www.benefityourliferesources.com.
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Dick's Sporting 소개

Dick's Sporting
PublicDick's Sporting Goods, Inc. is an American chain of sporting goods stores founded in 1948 by Richard "Dick" Stack. It is the largest sporting goods retailer in the United States and is listed on the Fortune 500.
10,001+
직원 수
Coraopolis
본사 위치
$2.8B
기업 가치
리뷰
10개 리뷰
3.7
10개 리뷰
워라밸
3.2
보상
3.4
문화
4.1
커리어
3.0
경영진
3.2
65%
지인 추천률
장점
Great team and coworkers
Fun and positive environment
Flexible hours
단점
Management issues
Long hours during busy periods
Limited advancement opportunities
연봉 정보
45개 데이터
Senior/L5
Staff/L6
Senior/L5 · Senior Data Engineer
4개 리포트
$160,295
총 연봉
기본급
$123,304
주식
-
보너스
-
$156,000
$163,835
면접 후기
후기 4개
난이도
3.3
/ 5
소요 기간
14-28주
경험
긍정 0%
보통 75%