Workato is the only integration and automation platform that is as simple as it is powerful — and because it’s built to power the largest enterprises, it is quite powerful.
Simultaneously, it’s a low-code/no-code platform. This empowers any user (dev/non-dev) to painlessly automate workflows across any apps and databases.
We’re proud to be named a leader by both Forrester and Gartner and trusted by 7,000+ of the world's top brands such as Box, Grab, Slack, and more. But what is most exciting is that this is only the beginning.
Ultimately, Workato believes in fostering a flexible, trust-oriented culture that empowers everyone to take full ownership of their roles. We are driven by innovation and looking for team players who want to actively build our company.
But, we also believe in balancing productivity with self-care. That’s why we offer all of our employees a vibrant and dynamic work environment along with a multitude of benefits they can enjoy inside and outside of their work lives.
If this sounds right up your alley, please submit an application. We look forward to getting to know you!
Also, feel free to check out why:
Business Insider named us an “enterprise startup to bet your career on”
Forbes’ Cloud 100 recognized us as one of the top 100 private cloud companies in the world
Deloitte Tech Fast 500 ranked us as the 17th fastest growing tech company in the Bay Area, and 96th in North America
Quartz ranked us the #1 best company for remote workers
As a Data Engineer, you will be a key member of the organization who will establish bridges between Infrastructure, Product Analytics, Business Technologies and GTM teams.
You will be responsible for conceptualizing, designing, and building data pipelines and services, while contributing towards the evolution of our data platform as a strategic asset for driving business decisions across the company.
Workato Data Platform is based on the industry leading technologies such as Snowflake Data Warehouse, Clickhouse Database, Apache Kafka and AWS (lambda, fargate, etc ) and Workato SAAS solution itself for collecting data from third party business supporting services.
We are currently working on upgrading the platform to meet new requirements of rapidly growing business such as:
Increase the confidence in analytical data.
Make a clearest possible vision of the user journey.
Minimize the gap between data emitting and data availability for analytical purposes.
We plan to achieve this by adopting some additional leading edge technologies such as DBT, Airflow, Kafka Streams, Kafka Connect and DataHub unified Metadata management Platform.
You will make a direct impact to this movement and together we will be able to push Workato data platform at least to the Level 4 of the maturity model.
Hold strong ownership on data assets and pipelines that provide actionable insights into customer, product, GTM, and other key business functions.
Design, implement and maintain scalable data pipelines and transformations using data from a variety of engineering and business systems.
Ensure and sustain Data Quality, ensure analytical data artifacts are always accurate and maintain SLAs.
Collaborate with Analysts, Product Owners and Stakeholders to fuel business intelligence with quality data models, increasing data accessibility and drive adoption of data.
5+ years of work experience building & maintaining data pipelines on data-heavy environments (Data Engineering, Backend with emphasis on data processing, Data Science with emphasis on infrastructure)
Fluent knowledge of SQL.
Strong knowledge of common analytical domain programming languages such as Python and Java.
Ability to articulate a point of view and good communication, collaborative demeanor with work experience in distributed multinational teams.
Deep background in data engineering and a proven track record of solving complex data problems and designing long-term solutions.
Advanced English level.
Experience with Data Pipeline Orchestration tools (Airflow, Dagster or similar).
Experience with Data Warehousing Solutions (Snowflake, Redshift, BigQuery).
Confidence in using Git, K8s and Terraform.
Production experience with PostgreSQL and Clickhouse.
Experience with building Data Quality checks and Data Integrity Rules.
Experience with Data Streaming and CDC (Confluent platform, Kafka Ecosystem, Apache Flink, Debezium).
Knowledge of Python Data Libraries such as Pandas.
Strong understanding of Data Privacy and Security (GDPR, CCPA).
