Senior Data Engineer – Azure Databricks

🔥 5 minutes ago

🇨🇷 Costa Rica – Remote

⏰ Full Time

🟠 Senior

☁️ Cloud Engineer

👻 Ghost score 10%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Applaudo

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2013

💼 Consulting

📣 Marketing

📦 Logistics

Consulting • Marketing • Logistics

Applaudo is a nearshore software development company that helps businesses accelerate their digital transformation. With over a decade of experience, Applaudo partners with some of the world’s most admired brands to streamline IT solutions, optimize delivery costs, and modernize their digital infrastructure. The company offers a range of services including innovation and experience design, development and maintenance, quality assurance, cloud solutions, artificial intelligence, and cybersecurity. Applaudo is known for its expertise in data solutions and app modernization, and has a strong presence in industries like sports, media and entertainment, retail, consumer packaged goods, and financial services. Headquartered in Austin, TX with offices in San Salvador, Applaudo blends top-tier software development talent with cutting-edge technology to drive business growth for its clients.

📋 Description

• Design, build, and maintain reliable, production-grade data pipelines using Azure Databricks and Python • Develop scalable data transformations using PySpark or Spark SQL • Ingest and process data from APIs, relational databases, cloud storage, and SFTP sources • Integrate data from ticketing, CRM, marketing, retail, and other enterprise platforms into a centralized Data Hub • Develop and maintain data warehouse structures, dimensional models, and data marts • Write, optimize, and maintain SQL transformations • Implement data quality validations and consistency checks • Build and maintain outbound data feeds for third-party systems • Investigate data discrepancies and troubleshoot pipeline issues • Document data pipelines, transformation logic, dependencies, and technical processes • Collaborate with team members and stakeholders to clarify requirements and identify risks • Communicate progress, blockers, and potential delays proactively, proposing next steps to keep deliverables on track

🎯 Requirements

• 5+ years of professional experience in Data Engineering • Strong hands-on experience with Azure Databricks • Advanced Python skills for data processing, transformation, and pipeline development • Solid experience with Apache Spark, including PySpark and/or Spark SQL • Advanced SQL proficiency, including complex transformations, data validation, and query performance optimization • Strong knowledge of data warehousing and dimensional modeling, including facts, dimensions, and data marts • Experience integrating data from REST APIs, relational databases, cloud storage, and SFTP • Hands-on experience with Microsoft Azure data services for data ingestion, storage, processing, and integration • Experience implementing data quality checks and validating consistency between source systems and target data models • Ability to troubleshoot pipeline failures, investigate data discrepancies, and implement reliable solutions • Strong problem-solving, analytical, and organizational skills • Ability to work autonomously, take ownership, and follow established engineering practices and documentation standards • Strong English communication skills and ability to collaborate with a US-based team • Experience migrating SQL-based data workloads to Azure Databricks is nice to have

🏖️ Benefits

• Remote work option • Opportunity to learn fast and work alongside strong teams • Modern ways of working • Participation in AI-native transformation at scale