Junior/Middle Data Engineer (Python)

About the Team

Here at Avride, we're building the future with self-driving vehicles and delivery robots. As you can imagine, this creates a massive amount of data, and we need someone to help us connect the dots. This is a great opportunity to join a growing team and have a real, tangible impact on our technology and our success.

About the Role

We’re looking for a Data Analytics Engineer who is skilled in both data engineering and data analysis. Your main goal will be to take the messy, raw data coming from our vehicles, APIs, and databases, and transform it into clean, reliable datasets that everyone from our engineers to our CEO can actually use. You'll be responsible for building the data pipelines that make this happen, and the dashboards that bring the insights to life.

What You'll Do

• You'll tame our raw data streams, building the ETL pipelines that pull information from all corners of the company into our data warehouse.
• You'll work with some truly unique datasets—everything from vehicle sensor telemetry and logistics data to the results of our internal system tests.
• You'll write the smart, efficient SQL queries needed to shape our data and prepare it for analysis.
• You'll create and manage the Grafana dashboards that our teams rely on to track performance and spot issues.
• You'll work closely with other teams to figure out what data they need, and then you'll deliver it.
• You'll also get to experiment with our internal AI and LLM-based tools to find new ways to analyze data and automate insights.

What You'll Need

• Strong, practical experience with Python and its data libraries (like Pandas, Polars, etc.).
• Expert-level SQL. You should be very comfortable with complex joins, window functions, and query optimization.
• A solid background in building and maintaining ETL pipelines using modern tools.
• Hands-on experience with a BI tool like Grafana, Tableau, or Looker.
• Familiarity with workflow orchestrators like Airflow, Dagster, or Prefect.
• A good high-level understanding of how Large Language Models (LLMs) work and an interest in applying them to data problems.
• A strong sense of ownership and a passion for making sure the data is right.

Nice to Have

• You've worked with ClickHouse or other modern analytical databases (Snowflake, BigQuery, Redshift).
• You have experience with vehicle, sensor, or logistics data.
• You've worked in the autonomous vehicle or robotics industry before.

#LI-MS1

About the Role

You’ll lead two core systems inside Veeam Data Command Center: the Knowledge Graph and the hyperscale data lake integrations. Together, they help customers understand where sensitive data lives, who can access it, how it moves, and whether AI models trained on it can be trusted. 

You’ll lead multiple teams building a searchable, security-aware graph that works at enterprise scale. This role is for a hands-on technical leader who can set clear direction, grow strong teams, and deliver reliable systems—while building an AI-first engineering culture with high standards for quality and security. 

What You’ll Do

• Set the technical vision and end-to-end architecture for the Knowledge Graph, including the data model, storage engine, and query layer at very large scale 

• Guide the evolution of the graph schema for data sources, identities, access, classifications, and lineage (property graph and/or RDF) using Amazon Neptune and/or Neo4j 

• Own the strategy for hyperscale lake and lakehouse integrations, including connectors and scanning engines that ingest metadata and lineage from Delta Lake, Iceberg, Parquet/Avro, and platforms like Azure Data Lake, AWS S3/Glue, and BigQuery without disrupting production 

• Drive performance and reliability, including standards for indexing, partitioning, and query planning, and tuning traversals and queries (Gremlin, Cypher/openCypher, SPARQL) 

• Build and scale an AI-first engineering approach where teams use tools like Claude Code, Cursor, and Copilot responsibly, with guardrails for security, maintainability, and code quality 

• Invest in reusable engineering building blocks (including “Claude skills” and agent workflows) that make teams faster and more consistent 

• Own delivery outcomes: roadmap execution, operational readiness, incident learning, and cross-team alignment 
• Hire, coach, and develop leaders, including engineering managers and senior/staff engineers, with clear expectations and growth paths 

What You’ll Bring

• 10+ years of software engineering experience in data infrastructure, graph systems, or distributed data platforms 

• 4+ years of engineering leadership experience, including leading through managers and scaling multiple teams 

• Strong production experience with Amazon Neptune and/or Neo4j, including scaling, operations, and trade-offs (property graph vs. RDF) 

• Proven ability to lead graph modeling for complex domains, including lineage and permissions at enterprise scale 

• Deep knowledge of Gremlin, Cypher/openCypher, and/or SPARQL, including performance tuning and query design best practices 

• Experience with data lakes/lakehouses (Delta Lake, Iceberg, Parquet) across major cloud platforms (Azure Data Lake, AWS S3/Glue, BigQuery) 

• Experience designing and operating distributed systems using tools like Spark, Flink, or Presto/Trino, with strong judgement on scalability and cost 

• Strong backend background in Go and/or Python, with the ability to review designs, guide decisions, and unblock teams 

• Practical experience using AI-assisted development tools and the ability to set standards that keep AI-assisted code secure and high quality 

Bonus Skills

• Experience operating graph systems at massive scale 

• Background in data security, access governance, and policy controls 

• Experience with AI/ML governance tools and practices (e.g., MLflow, Databricks Mosaic AI) 

• Experience building custom agents, MCP-based workflows, or reusable engineering automation 

• Infrastructure-as-Code experience (e.g., Terraform or Pulumi) 

• Contributions to graph standards or communities (GQL, openCypher, SPARQL)