Data Analytics Engineer

Openkrill; United States; Remote; USD 130,000 - 180,000; Full time

Job summary

DevSavant is seeking a Senior Analytics Engineer / Senior Data Ops Analyst for its Data Operations team. Remote-first role focusing on reliability, release quality, and client-facing data correctness at scale.

You will own data-product releases, manage multiple squads, and collaborate with Engineering and Data Science to ensure feasible, robust data solutions for external clients.

Qualifications

• 6+ years in data-focused roles.
• Expert SQL, with a track record of mining large datasets for inconsistencies.
• Experience at real scale: complex multi-table environments with frequent update cycles.
• Experience designing data quality test plans that hold up in aggregate.
• Comfort making defensible judgment calls when the outcome is unclear.
• Customer-facing experience translating client feedback into feasibility.

Responsibilities

• Monitor data quality at each stage of the pipeline and validate data in aggregate.
• Own end-to-end data product release: accurate, reliable, on-time.
• Project-manage cross-functional squads across multiple deadlines.
• Communicate action plans and timelines directly to clients.
• Collaborate with Data Science and Engineering to close feasibility gaps.

Skills

SQL proficiency

Data quality analysis

Cross-functional collaboration

Tools

Looker

Redash

BigQuery

Git

Python

Job description

ABOUT DEVSAVANT

DevSavant is an operating partner for startups and growth‑stage companies, helping them turn ambition into execution.

We support founders and leadership teams with product engineering and global staffing, from early prototypes and MVPs to scaling high‑performing teams. Our vetted talent across LATAM and Asia embeds directly into client teams, operating as true extensions rather than external vendors.

With over 8 years working in venture‑backed ecosystems, DevSavant is trusted to accelerate delivery, scale teams efficiently, and support companies as they reach their next milestone.

ABOUT THE ROLE

We are seeking a Senior Analytics Engineer / Senior Data Ops Analyst for the Data Operations team of a remote‑first analytics company where the data itself is the product.

This is a senior individual contributor role owning the reliability, release, and customer‑facing correctness of core data products. The numbers you sign off on are consumed by external clients, so the bar for defensible correctness is high. You will work with genuinely messy data at scale — billions of rows, weekly panel refreshes, daily configuration shifts — where the right answer is often a judgment call.

LATAM‑based, full‑time, US business hours. Reports to the VP of Data Operations.

KEY RESPONSIBILITIES

DATA QUALITY

• Monitor data quality at each stage of the pipeline and build scalable test plans that validate data in aggregate
• Distinguish real signal from data‑quality issues using hypothesis testing to rule out sources of discrepancy
• Run root‑cause investigations that end in prevention, not just a fix

RELEASE & PROJECT MANAGEMENT

• Own the end‑to‑end data product release: accurate, reliable, on‑time
• Project‑manage cross‑functional squads across multiple concurrent deadlines
• Communicate action plans and timelines directly to clients

PRODUCT & COLLABORATION

• Take DRI ownership of coverage expansion initiatives, from scoping to delivery
• Partner with Commercial to turn how customers use the data into frictionless solutions
• Work with Data Science and Engineering to close feasibility gaps
• Use AI to streamline workflows without adding QA overhead

CORE TECHNICAL STACK

• Data: Advanced SQL, DBT, YAML, Regex, Excel
• Warehouse & Cloud: BigQuery, GCP (Cloud Storage, Dataproc), complex ETL
• BI: Looker, Redash, git
• Plus: Python, panel/longitudinal/subscription data, pandas or PySpark exposure

REQUIRED QUALIFICATIONS

• 6+ years in data‑focused roles
• Expert SQL, with a track record of mining large datasets for inconsistencies
• Experience at real scale: complex multi‑table environments with frequent update cycles, not static extracts
• Experience designing data quality test plans that hold up in aggregate
• Comfort making defensible judgment calls when the outcome is genuinely unclear
• Direct collaboration with Engineering and Data Science
• Cross‑functional project management with accountability for business outcomes
• Customer‑facing experience translating client feedback into technical feasibility

NICE TO HAVE

• Managing data vendors: selection, negotiation, issue resolution
• Mentoring teammates on data operations best practices
• Background in data‑as‑a‑product, market intelligence, or syndicated data
• Prior experience in a small, remote‑first team