Lead Data Engineer

Vista

Lead Data Engineer responsible for designing operational data stores and analytical ecosystems for cloud-based solutions. Collaborate with engineering teams to optimize real-time data flows and support AI initiatives.

Posted 5/30/2026full-timeRemote • CzechiaSeniorWebsite

Tech Stack

Tools & technologies

AirflowAWSBigQueryCloudETLKafkaPythonSparkSQL

About the role

Key responsibilities & impact

Architect & Lead Operational Data Flows by designing and overseeing the implementation of an Operational Data Store (ODS)
Build low-latency data streams using technologies like Kafka or Flink to power embedded analytics directly within customer-facing applications
Establish "Data Contracts" with upstream engineering teams to ensure high availability and schema stability for all real-time operational flows
Own the transition and scaling of our Analytical Data Store (e.g., Snowflake), ensuring it is optimized for both performance and cost-efficiency
Modernize transformation layer by implementing robust ELT patterns and modular data modeling (using dbt and airflow)
Champion Data Governance, ensuring that every dashboard and report is backed by high-quality, audited, and well-documented data
Build the "Data Foundation" for Machine Learning, including development of Feature Stores and automated pipelines for model training and inference
Mentor and grow a high-performing engineering team, fostering a culture of "DataOps" where automation, testing, and observability are the default
Act as a strategic partner to Product and Executive leadership, translating complex technical roadmaps into clear business value

Requirements

What you’ll need

8+ years in Data Engineering, with at least 3+ years in a formal leadership or management role
Proven experience architecting cloud data warehouses (Snowflake, BigQuery, or Databricks)
Expert-level proficiency in Python (for automation/pipelines) and SQL (for complex modeling and optimization)
Proficiency in AWS infrastructure management and event-driven pipelines (Kinesis, IAM, Monitoring, and IaC frameworks)
Hands-on experience with stream processing tools (Kafka, Flink, or Spark Streaming)
Ability to design ELT/ETL architectures from scratch using dbt, with a focus on idempotency, scalability, and error handling.
Experience implementing data quality frameworks (e.g., Great Expectations, Monte Carlo) and ensuring compliance (GDPR/CCPA)
Experience in a "Product-led" organization where engineering is a value-driver
Ability to communicate complex architectural constraints (like latency or data consistency) to non-technical partners in terms of business impact and ROI
Proven track record of working with Product Managers to ship data-intensive features in an Agile environment

Benefits

Comp & perks

Remote-First operating model and culture
Collaboration spaces for team members to work physically together

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Hard Skills & Tools

Data EngineeringCloud Data WarehousingPythonSQLAWSKafkaFlinkdbtELTData Quality Frameworks

Soft Skills

LeadershipMentoringCommunicationStrategic PartnershipTeam BuildingData GovernanceProblem SolvingCollaborationAgile MethodologyCultural Development