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Senior Analytics Engineer

Posted 2024-10-17

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📍 Location: USA, UK, Philippines, Poland, South Africa

🔍 Industry: Finance and technology (remittances)

🏢 Company: Zepz

🗣️ Languages: English, Akuapem, Amharic, Bengali, Ewe, Fante, Ga, Igbo, Kalenjin, Luganda, Oromo, Somali, Swahili, Wolof, Bulgarian, Croatian, Czech, Danish, Dutch, Estonian, Finnish, French, German, Greek, Hungarian, Irish, Italian, Latvian, Lithuanian, Maltese, Polish, Portuguese, Romanian, Slovak, Slovenian, Spanish, Swedish

🪄 Skills: AWSPostgreSQLPythonSQLGCPMySQLSnowflakeTableauAirflowAzureCommunication SkillsProblem Solving

Requirements:
  • Experience with DBT to design and implement data models.
  • Experience using a modern data warehouse (e.g., Databricks, Snowflake, BigQuery) for scalable and efficient data processing.
  • Proficiency in SQL and experience with database technologies (e.g., PostgreSQL, MySQL, Redshift).
  • You have the ability to work confidently with the tools of modern software engineering, for example, working with the command line, version control, testing, and performing code reviews.
  • You have previous use of any orchestration tools similar to Airflow, DBT Cloud, or Fivetran.
  • Familiarity with data visualization tools (e.g., Mode, Tableau, Looker).
  • Familiarity with cloud platforms (e.g., AWS, GCP, Azure) and their data services.
  • You’re comfortable, or have an interest in, reading and extracting data transformation processes from Python scripts.
  • You see yourself as a problem-solver who wants to understand the business problem and communicate the commercial impact alongside the technical solution.
  • Strong communication skills and the ability to work effectively in a distributed team environment.
  • You are comfortable collaborating across multiple time zones.
  • You have an open mind with respect to diversity and inclusivity.
Responsibilities:
  • Developing, testing, and implementing data models to ensure data integrity and performance using DBT.
  • Collaborating with cross-functional teams to understand data needs and develop solutions to meet those needs.
  • Contributing to and following best practices for data consumption, including educating data consumers on data quality, availability and interpretation.
  • Optimizing existing data processes and pipelines to improve efficiency and reduce latency.
  • Identifying opportunities to reduce complexity and increase efficiency across data models and our data warehouse.
  • Troubleshooting and resolving data issues, ensuring data quality and reliability.
  • Ensuring the data and output is of high quality - tested, automated, scalable, and documented.
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