Big Data Consultant

Job Description:

• Strong People management, leadership, organizational skills. Outstanding communication (written and verbal) skills.
• Experience leveraging open-source tools, predictive analytics, machine learning, Advanced Statistics, and other data techniques to perform basic analyses.
• Demonstrated basic knowledge of statistical analytical techniques, coding, and data engineering.
• Experience developing and configuring dashboards is a plus.
• Demonstrated judgement when escalating issues to the project team.
• High proficiency in Python/Spark, Hadoop platforms & tools (Hive, Impala, Airflow, NiFi), SQL.
• Curiosity, creativity, and excitement for technology and innovation.
• Demonstrated quantitative and problem-solving abilities.
• Expert proficiency in using Python/Scala, Spark (tuning jobs), SQL, Hadoop platforms to build Big Data products & platforms.
• Comfortable in developing shell scripts for automation.
• Proficient in standard software development, such as version control, testing, and deployment.
• Experience with visualization tools like tableau, looker.
• At least 5 years leading collaborative work in complex engineering projects in an Agile setting e.g. Scrum.
• Extensive data warehousing/data lake development experience with strong data modelling and data integration experience.
• Good SQL and higher-level programming languages with solid knowledge of data mining, machine learning algorithms and tools.
• Strong hands-on experience in Analytics & Computer Science.
• Demonstrated basic knowledge of statistical analytical techniques, coding, and data engineering.
• Experience in building and deploying production-level data-driven applications and data processing workflows/pipelines and/or implementing machine learning systems at scale in Java, Scala, or Python and deliver analytics involving all phases like data ingestion, feature engineering, modelling, tuning, evaluating, monitoring, and presenting.
• At least 13+ years of relevant hands-on experience as a Data Engineer in an individual contributor capacity.
• Able to lead the implementation of machine learning production systems.
• Demonstrated ability, through hands-on experience, to develop production machine learning pipelines.