Big Data Engineer, Hadoop, Azure

Job not on LinkedIn

🕒 July 27

🔔 Pennsylvania – Remote

🔔 Pennsylvania – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🚰 Data Engineer

👻 Ghost score 27%

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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Martian Wall

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2019

🎯 Recruiter

💼 Consulting

🤝 B2B

Recruitment • Consulting • B2B

Martian Wall is a Nashik-headquartered recruitment and HR consulting firm that specializes in IT and non‑IT talent acquisition for Fortune 500 and other global clients. It provides tailored recruitment and HR consulting services—focused on talent acquisition and workforce optimization—across North America, South America, Europe and the United Kingdom, positioning itself as a B2B partner connecting top talent with multinational companies.

📋 Description

• Design, develop and maintain an optimal data pipeline architecture using both structured data sources and big data for both on-premise and cloud-based environments in both streaming and real time. • Develop and automate ETL code using scripting languages, ETL tools and job scheduling software to support all reporting and analytical data needs. • Design and build dimensional data models to support the data warehouse initiatives. • Assemble large, complex data sets that meet the analytical needs of the data science team. • Assess new data sources to better understand availability and quality of data. • Identify, design, and implement internal process improvements: automating manual processes, optimizing data pipeline performance, re-designing infrastructure for greater scalability and access to information. • Participate in requirements gathering sessions to distill technical requirements from business requests. • Collaborate with business partners to productionize, optimize, and scale enterprise analytics. • Collaborate with data architects and modelers on data store designs and best practices.

🎯 Requirements

• Five (5)+ years’ experience in traditional and modern Big Data technologies (HDFS, Hadoop, Hive, Pig, Sqoop, Kafka, Apache Spark, hBase, Oozie, No SQL databases, PostgreSQL, GIT, Python, REST API, Snowflake, etc.) • Two (2)+ years’ experience building data platforms using Azure stack (Azure Data Factory, Azure DataBricks, etc.) • Experience with object-oriented/object function scripting languages: Python, Java, C++, Scala • Experience extracting/querying/joining large data sets at scale • Experience utilizing Snowflake to build data marts with the data residing in Azure storage is a plus • Thorough understanding of relational, columnar and NoSQL database architectures and industry best practices for development • Understanding of dimensional data modeling for designing and building data warehouses • Excellent advanced SQL coding and performance tuning skills • Experience with parsing data formats such as XML/JSON and leveraging external APIs • Understanding of agile development methodologies • Ability to work in a team-oriented, collaborative environment; good interpersonal skills • Strong analytical and problem-solving skills; ability to weigh various suggested technical solutions against the original business needs and choose the most cost-effective solution • Keen attention to detail and ability to access impact of design changes prior to implementation • Self-driven, highly motivated and ability to learn quick • Ability to effectively prioritize and execute tasks in a high-pressure environment • Strong customer service orientation • Ability to present and explain technical information to diverse types of audiences in a way that establishes rapport and gains understanding • Work experience with geospatial data and spatial analytics is preferred.