Senior Data Engineer

Lytx, Inc.•San Diego, CA

7d•$136,000 - $172,000

About The Position

At Lytx, we make roadways safer by transforming real‑time video and telematics into actionable safety intelligence. The Data Platform is the backbone of that mission - ingesting and processing massive event streams, shaping trusted datasets, and serving low‑latency insights that power products and analytics across the company. As a Senior Data Engineer, you’ll design, build, and operate high‑scale streaming and batch pipelines, optimize our Redshift data lake, and raise the bar on data quality, reliability, and cost efficiency.

Requirements

6+ years of data engineering experience designing and operating production‑grade streaming + batch data systems at scale.
Deep hands‑on experience with Kafka and stream processing technology
Strong SQL and performance tuning in Redshift; practical experience with Postgres.
Production experience with Airflow (DAG design, SLAs, retries, idempotency) and Flink (job optimization).
Experience with open table formats - Apache Iceberg (preferred) including partition evolution, schema evolution; comfort with Athena query performance.
Solid data modeling for telemetry/events and analytics; understanding of data contracts, schema registry, and CDC patterns.
Proficiency in object-oriented programming languages such as C# / Java / Python; strong software engineering fundamentals (testing, CI/CD, code review).
AWS experience (S3, Redshift, Athena, IAM; EKS is a plus).
Clear communicator who owns outcomes, balances speed with rigor, and thrives in a collaborative, high‑impact environment.

Nice To Haves

Experience with IoT/telematics data.
Knowledge of dbt / Great Expectations (or other data testing frameworks).
Terraform/IaC, Kubernetes, and cost‑aware architecture.

Responsibilities

Build and operate streaming pipelines with Kafka and streaming technologies (i.e. Flink) for real‑time telemetry and event processing.
Design robust ETL with Airflow into Redshift and S3, supporting analytics, product use cases, and downstream services.
Model data (event/telemetry and serving schemas); own data contracts, schema evolution, and table partitioning/compaction strategies.
Optimize performance & cost across Redshift, Iceberg/Athena, and streaming jobs
Harden quality, reliability, and observability: SLAs/SLOs, lineage, testing, validation, alerting, and incident response for data pipelines and datasets.
Serve data to applications via APIs/GraphQL and scalable query services (Athena/Redshift), ensuring predictable latency and well‑documented contracts.
Collaborate cross‑functionally with application teams to translate business needs into durable, scalable data assets.
Mentor engineers and contribute to standards, patterns, and best practices; be a continuous learner who uplifts team capabilities.