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Lead Data Solutions Engineer

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At Algolia, we are committed to fostering a data-centric culture, and we are seeking a Lead Data Solutions Engineer to enhance our Data team and drive this initiative forward.

Important: This role is exclusively available to candidates residing in France, the UK, Romania, and the US, including fully remote positions. Applications from other regions will not be accepted.

Note: A minimum of 4 years of experience in Data Engineering is essential. Proficiency in Python, Airflow, and Spark is required.

Our Data team plays a crucial role in empowering other departments to make informed decisions by delivering accurate and reliable data. We collaborate closely with business analysts across Operations, Marketing, Product, and Infrastructure teams to uncover significant insights.

The Data team consists of a Data Engineering unit (focused on developing pipelines and optimizing the Data Lake & Warehouse), an Analytics Engineering unit (dedicated to creating standardized data models), and Data Analysts.

As a member of the Data Engineering unit, your daily tasks will include:

  • Crafting data pipelines and ETL workflows primarily using Python, Airflow (AWS MWAA), Spark (AWS Glue, EMR), and other AWS services managed via Terraform.
  • Enhancing and maintaining our data warehouse (AWS Redshift).
  • Assisting the Analytics Engineering unit in constructing data models with dbt for use in business reporting.
  • Collaborating with Engineering and Business teams to gather requirements.

We embrace an open-minded philosophy and actively explore new technologies to meet our objectives, continuously evaluating our choices in this regard. You will have the chance to influence decisions in this area.

As a senior member of the team, you will also be responsible for mentoring junior engineers and guiding their professional development at Algolia.

With Algolia's rapid expansion, numerous data-related challenges await. Are you prepared to tackle them?

Key responsibilities:

  • Design, develop, enhance, and scale our data pipelines.
  • Collaborate with engineers, data analysts, and business analysts to capture and model data effectively.
  • Monitor data integrity and growth metrics.
  • Contribute to the improvement of our data lake and data warehouse architecture to enhance performance, simplicity, and user autonomy.
  • Assist analysts in industrializing reports and dashboards to boost organizational productivity.
  • Ensure that every product is launched with a data-driven strategy.

You might be a suitable candidate if you possess:

  • Extensive experience in designing and constructing data pipelines.
  • Proficiency in orchestrating pipelines using Airflow.
  • Strong command of Python programming.
  • Significant experience with cloud platforms and their architecture.
  • In-depth knowledge of data warehouses.
  • Familiarity with infrastructure aspects pertinent to data engineering.
  • Interest in Big Data challenges (approximately 600Tb of data, over 14Tb weekly, around 700M files, more than 1.2M weekly, approximately 300 dbt models, and around 70 Airflow DAGs).
  • A keen interest in understanding data and business requirements.
  • Excellent proficiency in spoken and written English.
  • A humble, curious, and proactive mindset, balancing creativity, resourcefulness, and practicality.

Preferred qualifications:

  • Experience with AWS Redshift management.
  • Familiarity with parallel data processing frameworks such as Apache Spark.
  • Experience in assessing data quality using dbt.
  • Experience in high-growth environments ($50-200M ARR range, significant changes, and developing internal infrastructure).