Principal Data Engineer

Through proprietary software and AI, along with a focus on customer delight, Sleek makes the back-office easy for micro SMEs. We give Entrepreneurs time back to focus on what they love doing - growing their business and being with customers. With a surging number of Entrepreneurs globally, we are innovating in a highly lucrative space. We operate 3 business segments: **Corporate Secretary**: Automating the company incorporation, secretarial, filing, Nominee Director, mailroom and immigration processes via custom online robots and SleekSign. We are the market leaders in Singapore with ~5% market share of all new business incorporations **Accounting & Bookkeeping**: Redefining what it means to do Accounting, Bookkeeping, Tax and Payroll thanks to our proprietary SleekBooks ledger, AI tools and exceptional customer service **FinTech payments**: Overcoming a key challenge for Entrepreneurs by offering digital banking services to new businesses Sleek launched in 2017 and now has around 15,000 customers across our offices in Singapore, Hong Kong, Australia and the UK. We have around 500 staff with an intact startup mindset. We have recently raised Series B financing off the back of >70% compound annual growth in Revenue over the last 5 years. Sleek has been recognised by The Financial Times, The Straits Times, Forbes and LinkedIn as one of the fastest growing companies in Asia. Backed by world-class investors, we are on track to be one of the few cash flow positive, tech-enabled unicorns based out of Singapore. **Requirements**: We are looking for a Principal Data Engineer that is excited about the below Mission and Outcomes over the next 6-12 months. **Mission**: Work closely with cross-functional teams to translate our business vision into impactful data solutions. Drive the alignment of data architecture requirements with strategic goals, ensuring each solution not only meets analytical needs but also advances our core objectives. Be pivotal in bridging the gap between business insights and technical execution by tackling complex challenges in data integration, modeling, and security, and by setting the stage for exceptional data performance and insights. Shape the data roadmap, influence design decisions, and empower our team to deliver innovative, scalable, high-quality data solutions every day. **Outcomes**: **1. Architecture & Design**: - Define the overall greenfield data architecture (batch + streaming) using GCP - BigQuery.. - Establish best practices for ingestion, transformation, data quality, and governance. **2. Data Ingestion & Processing**: - Lead the design and implementation of ETL/ELT pipelines: - Ingestion: Datastream, Pub/Sub, Dataflow, Airbyte, Fivetran, Rivery - Storage & Compute: BigQuery, GCS - Transformations: dbt, Cloud Composer (Airflow), Dagster - Ensure data quality and reliability with dbt tests, Great Expectations/Soda, and monitoring. **3. Governance & Security**: - Implement Dataplex & Data Catalog for metadata, lineage, and discoverability. - Define IAM policies, row/column-level security, DLP strategies, and compliance controls. **4. Monitoring, Observability & Reliability**: - Define and enforce SLAs, SLOs, and SLIs for pipelines and data products. - Implement observability tooling: - Cloud-native: Cloud Monitoring, Logging, Error Reporting, Cloud Trace. - Third-party (nice-to-have): Monte Carlo, Datafold, Databand, Bigeye. - Build alerting and incident response playbooks for data failures and anomalies. - Ensure pipeline resilience (idempotency, retries, backfills, incremental loads). - Establish disaster recovery and high availability strategies (multi-region storage, backup/restore policies). **5. Analytics Enablement**: - Partner with BI/analytics teams to deliver governed self-service through Looker, Looker Studio, and other tools. - Support squad-level data product ownership with clear contracts and SLAs. **6. Team Leadership** - Mentor a small data engineering team; set coding, CI/CD, and operational standards. - Collaborate with squads, product managers, and leadership to deliver trusted data. To do this you will have: - 10+ years experience in data engineering, architecture, or platform roles. - Strong expertise in GCP data stack: BigQuery, GCS, Dataplex, Data Catalog, Pub/Sub, Dataflow. - Hands-on experience building ETL/ELT pipelines with dbt + orchestration (Composer/Airflow/Dagster). - Deep knowledge of data modeling, warehousing, partitioning/clustering strategies. - Experience with monitoring, reliability engineering, and observability for data systems. - Familiarity with data governance, lineage, and security policies (IAM, DLP, encryption). Strong SQL skills and solid knowledge of Python for data engineering. Nice-to-Have - Experience with Snowflake, Databricks, AWS (Redshift, Glue, Athena), or Azure Synapse. - Knowledge of open-source catalogs (DataHub, Amundsen, OpenMetadata). - Background in streaming systems (Kafka, Kinesis, Flink,