What you will be doing:
- Design and implement data models that support analytical and reporting requirements.
- Collaborate with data analysts and business stakeholders to understand data needs and translate them into technical requirements.
- Participate in design and architecture discussions to ensure data requirements are accurately captured and implemented.
- Develop and maintain clear documentation of data sources, data flows, and data transformations in collaboration with the engineering team.
- Ensure data integrity and consistency by working with engineers to implement best practices for data collection, storage, and retrieval.
- Ensure data quality and consistency across various data sources and systems.
- Work closely with engineering managers, engineers, product managers and business analysts to understand and address their data needs.
- Act as a subject matter expert on analytics engineering best practices and tools.
- Mentor junior team members and provide technical guidance and support.
What you should bring:
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
- Minimum of 8 years of experience in analytics engineering, analyst or a related role.
- Proficiency in SQL and experience with relational databases (e.g., PostgreSQL, MySQL).
- Strong programming skills in Python or a similar language.
- Strong familiarity with any one of cloud data warehousing solutions is a must (e.g., Snowflake, Redshift, BigQuery).
- Experience with big data technologies (e.g., Hadoop, Spark, Kafka) and cloud platforms (e.g., AWS, GCP, Azure).
- Familiarity with risk management concepts and analytics methodologies in the fintech domain.
- Experience with version control systems (e.g., Git) and agile development methodologies is a plus.
- Familiarity with other data engineering tools, such as Apache Airflow, Databricks is a plus.
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