Data Engineer

Data Engineer (Mid–Senior Level)

Location:
Bay Area preferred (Hybrid / Onsite a few days per week)

Employment Type:
Contract-to-Hire

Seniority:
Mid- Senior

Note: No C2C or C2H arrangements will be considered. This is a direct contract engagement only. No agencies—unsolicited outreach will be reported as spam.

About the Role

We are seeking a Mid- Senior level
Data Engineer
to join a fast-growing, multidisciplinary data team responsible for building high-quality data systems that power internal analytics, operational tooling, and user-facing product experiences.

In this role, you'll design and implement scalable data pipelines, shape data architecture, and ensure the reliability, accessibility, and performance of our cloud-based data environment. You'll collaborate closely with product, analytics, hardware, and software engineering teams to manage data flows across a complex ecosystem—including IoT devices, backend systems, and customer-facing applications.

We're looking for someone who thrives in a fast-paced startup environment and is genuinely passionate about building technology that supports sustainability.

This position can flex to mid-level, senior, or near-staff responsibilities.

Key Responsibilities

Data Pipeline Engineering

● Develop, test, and deploy
scalable, reliable data pipelines
built on AWS and Snowflake.

● Design and implement frameworks for ingesting, transforming, and delivering data for product, analytics, operations, and ML/AI use cases.

Cloud & Warehouse Architecture

● Architect, implement, and maintain data warehouse structures, schemas, and models in
Snowflake
.

● Optimize Snowflake compute and storage efficiency, including
query tuning, clustering keys, scaling strategies
, and warehouse governance.

Data Platform Ownership

● Build and maintain data orchestration workflows using Python, SQL, and cloud-native services.

● Manage and enhance
RBAC
, data security controls, and data access patterns across the organization.

● Contribute to infrastructure-as-code implementations using tools like
Terraform or AWS CDK
.

Cross-Functional Collaboration

● Work closely with analytics, product, engineering, and hardware teams to define data requirements and support new features.

● Recommend best practices for data quality, observability, lineage, and governance across all data pipelines and sources.

Technical Leadership

● Advise on long-term data architecture strategy and tooling selection.

● Lead design reviews, mentor junior data engineers, and help establish engineering standards for the data discipline.

● Architect solutions for large-scale IoT datasets, high-throughput pipelines, and real-time or near real-time analytics.

Required Qualifications

●
3–10+ years
of experience as a Data Engineer or Software Engineer working on cloud-based data systems (mid–senior range considered).

● Strong hands-on experience with
Snowflake
(administration, performance tuning, RBAC, schema design).

● Strong experience with
AWS
(e.g., Lambda, S3, Glue, Step Functions, ECS, or similar services).

● Advanced proficiency in
Python
and
SQL
, including debugging, testing, and building production-grade data workflows.

● Experience building data pipelines, ETL/ELT processes, and scalable ingestion/transformation systems.

● Familiarity with
Infrastructure as Code
(CDK, Terraform, or similar).

Preferred Qualifications

● Experience with
IoT data
, event-driven architectures, or large-scale device telemetry.

● Familiarity with Customer Data Platforms (CDPs), BI/reporting systems, or analytics tooling.

● Understanding of data governance best practices (lineage, cataloging, observability).

● Experience with modern data orchestration tools (Airflow, Dagster, dbt, Prefect, etc.).

● Passion for sustainability.

What Success Looks Like

● Data pipelines are scalable, observable, and reliable as data volume and product needs grow.

● Snowflake is optimized for fast, cost-efficient queries with clear governance and access patterns.

● Product, analytics, and engineering teams can depend on consistent, high-quality data for decision-making and user-facing features.

● (For senior-level hires) You establish best practices, mentor teammates, and help define the long-term data strategy.