We are seeking for a skilled Data Engineer to join us in a groundbreaking initiative at the intersection of data engineering, machine learning, and proprietary nutrition. In this role, you will play a key part in shaping the future of wellness by developing an innovative nutrition database. Join a company with ambitious goals, aiming for a multi-billion USD valuation while making a meaningful and lasting impact on health and wellness.

As a Data Engineer, you will be tasked with designing, developing, testing, and maintaining systems that enable the acquisition, storage, processing, and retrieval of data for analytics, machine learning, and various applications. You will collaborate closely with data scientists, application developers, and machine learning engineers to understand their data requirements and ensure the delivery of a robust and efficient data pipeline. In this role, you'll focus on building scalable, high-performance data infrastructures while ensuring data is highly available, reliable, and optimized for business insights. You will also play a key role in implementing best practices for data management, security, and governance.

Collaborate with product teams and business stakeholders to gather data requirements Participate in sprint planning, backlog grooming, and other agile activities to align with project goals and technical specifications.

Work closely with cross-functional teams to understand project dependencies and ensure smooth execution of data engineering deliverables.

Design, develop, and maintain the organization’s data infrastructure, including data platforms, databases, and analytics tools, while ensuring compliance with data governance and security best practices (e.g., GDPR).

Build and maintain scalable data pipelines for data ingestion, transformation, and storage, optimizing performance and reliability.

Implement data engineering solutions to prepare analytics-ready datasets for analysis and reporting.

Build data models to support various consumption patterns, ensuring efficient data access for different use cases.

Build feature engineering pipelines to integrate machine learning algorithms into the data ecosystem. Integrate machine learning models with data pipelines for seamless deployment and scalability.

Implement data quality checks, validation rules, and cleansing processes to ensure accuracy.

Maintain documentation for platform architecture, data models, data engineering processes, and data lineage.

Participate in Data Operations ensuring adherence to SLA and data quality standards.

5+ years of combined experience in data engineering, data warehousing, and data science.

Bachelor’s degree in Computer Science, Engineering, or equivalent work experience (Master’s degree preferred.

Expertise in data modeling, ETL processes, and data warehousing concepts.

Strong proficiency in programming languages such as Python and Scala.

Experience with big data technologies and frameworks like Hadoop and Spark

Hands-on experience with any of the major cloud providers (AWS, Google Cloud, Azure).

Proven experience building data lakes using Databricks, Snowflake, or AWS Redshift.

Databricks Data Engineering Professional certification is a strong plus.

Proficiency in SQL and relational databases such as SQL Server, PostgreSQL, and My SQL.

Experience developing data pipelines with technologies such as Airflow, Dagster, and Kafka.

Solid understanding of data security and privacy regulations.

Strong understanding of software development life cycle best practices, including test-driven development, continuous integration/deployment, and code security scanning.

Excellent problem-solving skills with a strong attention to detail, and the ability to adapt to evolving requirements.

Ability to navigate ambiguity and drive data engineering tasks to success without relying solely on formal authority.

Proven experience supporting data operations and managing SLAs in a production environment.

Outstanding communication and collaboration skills.

Ability to work independently and manage multiple tasks efficiently.

Positive, can-do attitude with a high-intensity work ethic and commitment to success.

Fast learner with the ability and interest to absorb new information, particularly in the nutrition domain.

5+years of experience in Python

5+ years of experience in SQL

5+ years of experience in PySpark

Implementing data engineering on Databricks

Hands-on experience with AWS tech stack

Working on PostgreSQL database

Developing data pipelines using frameworks such as Airflow/Dagster/Kafka

Building data models to structure the data to support various consumption patterns of the data (application consumption, analytics consumption etc.

Participating in agile methodologies for engineering project execution

Data access management and governance

$150,000 - $175,000 a year