Senior Data Engineer

What You'll Build

Core Responsibilities

Data Architecture & Infrastructure (40%)

Design and implement a multi-database architecture (MongoDB, Redis, Milvus, Neo4j, BigQuery)

Build scalable data pipelines for real-time conversation processing and personalization

Architect ETL/ELT workflows for data migration from legacy systems

Implement data partitioning, sharding, and optimization strategies for high-throughput systems

Create data governance frameworks ensuring quality, security, and compliance Vector & Graph Database Systems (25%)

Design and optimize Milvus vector collections for semantic search (1024-dim embeddings)

Build graph schemas in Neo4j for customer journey mapping and persona relationships

Implement HNSW indexing strategies and similarity search optimization

Create hybrid search systems combining vector, full-text, and graph queries

Monitor and tune database performance (query latency, throughput, resource utilization)

ML Data Infrastructure (20%)

Build data collection pipelines for LLM fine-tuning (conversation logs, tool executions)

Create feature stores for GNN training (customer interactions, engagement signals)

Implement data versioning and lineage tracking for ML experiments

Design A/B testing data infrastructure with CUPED variance reduction

Build real-time feature computation pipelines for contextual bandits

Analytics & Monitoring (15%)

Design BigQuery schemas for marketing analytics and performance tracking

Create materialized views and aggregation pipelines for real-time dashboards

Implement data quality monitoring and anomaly detection

Build observability infrastructure (Prometheus metrics, Grafana dashboards)

Develop cost optimization strategies for cloud data warehousing

Technical Stack You'll Work With

Databases & Storage

MongoDB (conversation state, active sessions)

Redis (caching, rate limiting, real-time data)

Milvus (vector embeddings, semantic search)

Neo4j (customer journey graphs, persona networks)

BigQuery (analytics warehouse, historical data)

Data Processing & Orchestration

Apache Airflow or Prefect (workflow orchestration)

Pandas, Polars (data transformation)

Apache Spark (optional - for large-scale processing)

dbt (data transformation and modeling)

ML/AI Data Pipeline

vLLM (LLM inference serving)

MLflow (model registry, experiment tracking)

Sentence Transformers (embedding generation)

PyTorch, TensorFlow (ML model training)

Cloud & Infrastructure

Google Cloud Platform (BigQuery, Cloud Storage, Compute)

Docker & Kubernetes (containerization, orchestration)

Terraform (infrastructure as code)

GitHub Actions or GitLab CI (CI/CD pipelines)

Programming & Tools

Python 3.10+ (primary language)

SQL (complex queries, query optimization)

Shell scripting (Bash/Zsh)

Git (version control)

Requirements

Must-Have Skills

5+ years of data engineering experience with production systems

Expert-level SQL and database design skills

Strong Python programming (async/await, type hints, testing)

Experience with at least 3 different database technologies (SQL, NoSQL, Vector, Graph)

Proven track record building high-scale data pipelines (>1M records/day)

Deep understanding of data modeling (dimensional, normalized, denormalized)

Experience with cloud data warehouses (BigQuery, Redshift, or Snowflake)

Strong knowledge of data quality, validation, and governance

Excellent debugging and optimization skills

Highly Desirable

Experience with vector databases (Milvus, Pinecone, Weaviate, Qdrant)

Experience with graph databases (Neo4j, ArangoDB, Neptune)

Knowledge of embedding models and semantic search

Experience with ML data pipelines (feature stores, model training data)

Understanding of A/B testing and experimental design

Experience with real-time streaming (Kafka, Pub/Sub, Kinesis)

Knowledge of LLMs and conversational AI systems

Experience with data migration projects (especially large-scale)

Background in marketing technology or customer data platforms

Nice-to-Have

Experience with PyTorch Geometric or graph neural networks

Knowledge of marketing analytics (attribution, segmentation, personalization)

Familiarity with LangChain, LangGraph, or agent frameworks

Experience with cost optimization in cloud environments

Contributions to open-source data engineering projects

Experience with data compliance (GDPR, CCPA)

Key Projects You'll Own

Phase 1: Foundation

Migrate 10M+ conversation vectors from Pinecone to Milvus

Design and implement MongoDB schemas for real-time agent state

Set up Neo4j graph database with customer journey models

Create BigQuery data warehouse with partitioned tables

Phase 2: Optimization

Build automated data quality monitoring system

Implement caching strategies (Redis) for 10x latency reduction

Optimize vector search queries (target: <50ms p95 latency)

Create real-time analytics dashboards (Grafana)

Phase 3: ML Infrastructure

Build LLM fine-tuning data pipeline

Implement feature store for GNN training

Create A/B testing data infrastructure

Design multi-armed bandit state management

Work Environment

Collaborative team: Work with ML engineers, backend developers, and data scientists

Modern stack: Latest technologies and tools

Impact: Your work directly affects millions of marketing interactions

Autonomy: Own your projects end-to-end

Growth: Clear path to Senior/Lead/Principal roles