Senior Data Engineer

Datasite and its associated businesses are the global center for facilitating economic value creation for companies across the globe. From data rooms to AI deal sourcing

and more. Here you'll find the finest technological pioneers: Datasite, Blueflame AI, Firmex, Grata, and Sherpany. They all, collectively, define the future for business growth.

Apply for one position or as many as you like. Talent doesn't always just go in one direction or fit in a single box. We're happy to see whatever your superpower is and find the best place for it to flourish.

Get started now, we look forward to meeting you.

Job Description

Grata is the leading private-market dealmaking platform—bringing the most comprehensive, accurate, and searchable proprietary data on private companies, financials, and owners to investors, advisers, and corporate deal teams. With 700+ customers and recognition from G2 and PE Wire, we're growing fast and shaping the future of data-driven dealmaking.

We're looking for a Senior Data Engineer to design, scale, and own the data platforms that power Grata's products and analytics. You'll lead the development of reliable batch and streaming pipelines, evolve our lakehouse and warehouse models, improve data quality and governance, and mentor engineers—partnering closely with Product, Data Science, and Application Engineering to ship business-critical capabilities.

Responsibilities

Own mission-critical pipelines: Design, build, and operate performant ELT/ETL in Python/SQL/Spark on Databricks (and related orchestration), with strong SLAs and clear data contracts.
Evolve the lakehouse & warehouse: Drive dimensional modeling/star schemas and incremental patterns (e.g., Delta Lake/CDC), balancing cost, performance, and usability.
Streaming & event data: Architect and run real-time/near-real-time jobs where it delivers product value; set the bar for idempotency and exactly-once semantics.
Quality, lineage, and governance: Implement automated testing, anomaly detection, validation, lineage/metadata, and documentation.
Scale & reliability: Establish SLOs, on-call rotations for data platforms, robust monitoring/alerting, and capacity/cost management.
Partner across functions: Work with Product and DS on source selection, feature readiness, and experiment design; with App Eng to expose data via stable APIs and semantic layers.
Mentor & uplift: Coach Data Engineers (and adjacent SWE/Analytics Eng), review designs/PRs, and lead brown-bag sessions to raise the team's technical bar. (Builds on our culture of sharing knowledge and collaboration.)
Ship outcomes: Break down work, sequence delivery, and land measurable improvements to freshness, completeness, and query performance.

Qualifications

6+ years building and operating production data systems at scale.
Deep fluency with Python and SQL; expert in Spark/Databricks and lakehouse patterns.
Strong data modeling skills
Experience running workloads in AWS (or similar cloud): storage, compute, networking basics, cost controls.
Hands-on with orchestration (Airflow/Databricks Workflows/dbt), CI/CD for data, and IaC.
Proven track record mentoring engineers and leading ambiguous initiatives to clear results.

Bonus

Event platforms (Kafka/Kinesis), vector/feature stores, ML feature engineering with DS partners.
Data observability platforms; data catalog/lineage tooling; data access governance.
Experience shaping product-facing datasets and semantic layers (e.g., for BI and APIs).

Our company is committed to fostering a diverse and inclusive workforce where all individuals are respected and valued. We are an equal opportunity employer and make all employment decisions without regard to race, color, religion, sex, gender identity, sexual orientation, age, national origin, disability, protected veteran status, or any other protected characteristic. We encourage applications from candidates of all backgrounds and are dedicated to building teams that reflect the diversity of our communities.

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