Design, build and maintain batch or real-time data pipelines in production.
Maintain and optimize the data infrastructure required for accurate extraction, transformation, and loading of data from a wide variety of data sources.
Develop ETL (extract, transform, load) processes to help extract and manipulate data from multiple sources.
Automate data workflows such as data ingestion, aggregation, and ETL processing.
Prepare raw data in Data Warehouses into a consumable dataset for both technical and non-technical stakeholders.
Partner with data scientists and functional leaders in sales, marketing, and product to deploy machine learning models in production.
Build, maintain, and deploy data products for analytics and data science teams on cloud platforms (e.g. AWS, Azure, GCP).
Ensure data accuracy, integrity, privacy, security, and compliance through quality control procedures.
Monitor data systems performance and implement optimization strategies.
Leverage data controls to maintain data privacy, security, compliance, and quality for allocated areas of ownership.

You have what it takes if you have...

5+ years of SQL skills and experience with relational databases and database design.
Experience working with cloud Data Warehouse solutions - Databricks, Apache Spark.
Experience working with data ingestion tools such as Fivetran, stitch, or Matillion.
Working knowledge of Cloud-based solutions (e.g. AWS, Azure, GCP).
Experience building and deploying machine learning models in production.
Strong proficiency in object-oriented languages: Python, Java, C++, Scala.
Strong proficiency in scripting languages like Bash.
Strong proficiency in data pipeline and workflow management tools (e.g., Airflow).
Strong project management and organizational skills.
Excellent problem-solving, communication, and organizational skills.
Proven ability to work independently and with a team.

Extra dose of awesome if you have...

Good understanding of NoSQL databases like CrateDB, Redis, Cassandra, MongoDB, or Neo4j.
Experience with working on large data sets and distributed computing (e.

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