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Entry Level Databricks Data Engineer Jobs in Kansas City, MO

Pwc

AI & GenAI Data Scientist - Manager

Kansas City, MO

$99K - $232K/yr

In this role at PwC, you will apply data, algorithms, and software engineering to build and deploy ... PwC does not intend to hire experienced or entry level job seekers who will need, now or in the ...

New

T-Mobile

Assoc Engineer, Data

Overland Park, KS · On-site

$71K - $128K/yr

Data Engineering (Required) * Data Modeling (Required) * Data Pipelines (Required ... Databricks DBRX (Required) * Microsoft Azure (Required) * Root Cause Identification (Required) * At ...

T-Mobile

Assoc Engineer, Data

Overland Park, KS · On-site

$71K - $128K/yr

Data Engineering (Required) * Data Modeling (Required) * Data Pipelines (Required ... Databricks DBRX (Required) * Microsoft Azure (Required) * Root Cause Identification (Required) * At ...

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Entry Level Databricks Data Engineer information

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$29.3K

$67.7K

$115.1K

How much do entry level databricks data engineer jobs pay per year?

As of Aug 26, 2026, the average yearly pay for entry level databricks data engineer in Kansas City, MO is $67,685.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,300.00 and $76,600.00 per year, depending on experience, location, and employer.

What is an entry level Databricks data engineer?

An Entry Level Databricks Data Engineer is a professional who uses Databricks, a cloud-based data analytics platform, to design, build, and maintain data pipelines. They are responsible for preparing and processing large datasets, ensuring data quality, and enabling analytics and machine learning workflows. Typically, they work with tools such as Apache Spark, SQL, and Python, and collaborate with data analysts and data scientists to deliver data-driven solutions. As entry-level engineers, they are expected to have foundational knowledge of data engineering concepts and be eager to learn more advanced techniques on the job.

What are the key skills and qualifications needed to thrive as an entry level Databricks data engineer?

To thrive as an Entry Level Databricks Data Engineer, you need a foundational understanding of data engineering concepts, SQL, and Python or Scala, typically supported by a relevant degree in computer science or a related field. Familiarity with Databricks, Apache Spark, cloud platforms (like AWS or Azure), and optional certifications such as Databricks Data Engineer Associate are highly valuable. Strong analytical thinking, attention to detail, and effective communication skills help you collaborate with teams and solve complex data challenges. These skills and qualities are essential for building reliable data pipelines, ensuring data quality, and delivering actionable insights in a fast-paced environment.

What are some common challenges faced by entry level Databricks data engineers, and how can they effectively overcome them?

Entry-level Databricks Data Engineers often face challenges such as learning to optimize Apache Spark jobs, managing complex data pipelines, and understanding cloud-based workflows. To overcome these, it's important to dedicate time to hands-on practice with Databricks notebooks, collaborate closely with more experienced engineers, and actively participate in code reviews and team discussions. Leveraging Databricks' extensive documentation and community forums can also help troubleshoot issues and stay updated on best practices.

What are the most commonly searched types of Databricks Data Engineer jobs in Kansas City, MO?

The most popular types of Databricks Data Engineer jobs in Kansas City, MO are:

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For Entry Level Databricks Data Engineer jobs in Kansas City, MO, the most frequently searched job titles are:

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Entry Level Databricks Data Engineer jobs near you

Data Engineer: Azure, Databricks & Snowflake | Onsite/Hybrid

Tech Mirrors

Overland Park, KS • On-site

$108K - $130K/yr

Other

Posted 8 days ago

Job description

Role

Data Engineer (Azure | Databricks | Snowflake)

Location: Overland Park, KS (Onsite/Hybrid)

Key Responsibilities
  • Build and enhance ingestion pipelines for large batch and event‑driven paths, integrating data from third‑party enrichment vendors, Digital platforms via Conversion API, Rewards/Promotions systems, and other sources.
  • Ensure data quality, reliability & operations by implementing validation, idempotency, replay/backfill strategies and deduplication to prevent quality drift.
  • Own monitoring, alerting, dashboarding and operational readiness, including wrappers around core pipelines.
  • Troubleshoot failures with root‑cause analysis, interpreting Spark logs, diagnosing performance issues (shuffle, skew, partitioning) and improving stability and SLA adherence.
  • Apply privacy, compliance and governance requirements across pipelines and datasets, supporting standards such as Unity Catalog, lineage, access controls, and PII handling.
  • Design pipelines with cost awareness from day one: cluster sizing, workload tuning, efficient compute/storage usage and balancing cost vs quality vs SLA.
  • Work collaboratively in a small, fast‑moving team, self‑driven and ownership‑oriented, raising and managing data quality escalations when issues are detected.
Required Skills
  • Strong coding in PySpark and SQL with hands‑on experience.
  • Databricks: notebooks, jobs, performance tuning fundamentals, medallion patterns; Spark fundamentals (partitioning, skew/shuffle optimization, log analysis).
  • Snowflake: data modeling and usage for analytics and warehousing workloads.
  • Azure ecosystem: Azure Data Factory orchestration and Azure‑native integrations.
  • Data engineering reliability patterns: validation, idempotency, replay/backfills, deduplication, auditability.
  • Data governance: Unity Catalog (preferred), lineage, access control patterns, PII handling.
  • Ownership mindset: ability to execute independently without constant approvals or check‑ins.
Nice‑to‑Have Skills
  • Event‑driven/streaming ingestion exposure, including Delta Live Tables (DLT).
  • Experience building config‑driven export frameworks for downstream consumers/vendors.
  • Interest in identity resolution concepts.
  • Operational telemetry: dashboards, alerts, SLA monitoring.
Success Criteria

Ships reliable, well‑governed datasets with strong data quality practices; scales pipelines for very large volumes; prevents silent failures where quality degrades; balances delivery speed with compliance, governance, and cost controls.

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